Blockchain-based Interoperable Healthcare using Zero-Knowledge Proofs and Proxy Re-Encryption

In the present technological era, healthcare providers generate huge amounts of clinical data on a daily basis. Generated clinical data is stored digitally in the form of Electronic Health Record (EHR) as a central data repository of hospitals. Data contained in EHR is not only used for the patients' primary care but also for various secondary purposes such as clinical research, automated disease surveillance and clinical audits for quality enhancement. Using EHR data for secondary purposes without consent or in some cases even with consent creates privacy issues. Secondly, EHR data is also made accessible to various stakeholders including different government agencies at various geographical sites through wired or wireless networks. Sharing of EHR across multiple agencies makes it vulnerable to cyber attacks and also makes it difficult to implement strict privacy laws as in some cases data is shared with organization that is governed by specific regional law. Privacy of individuals could be severely affected when their sensitive private information contained in EHR is leaked or exposed to the public. Data leaks can cause financial losses or an individual may encounter social boycott if his / her medical condition is exposed in public. To protect patients personal data from such threats, there exists different privacy regulations such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA) and My Health Record (MHR). However, continually evolving state-of-the-art techniques in Machine Learning (ML), Data Analytics (DA) and hacking are making it even more difficult to completely protect an individual's/patient's privacy. In this article, we have systematically examined various secondary uses of EHR with the aim to highlight how these secondary uses affect patients' privacy. Secondly, we have critically analyzed GDPR & HIPAA regulations and highlighted their possible areas of improvement, considering escalating use of technology and different secondary uses of EHR.

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

The high take-up rate of artificial intelligence (AI) by different sectors has triggered critical interest in data security, privacy, and ethics compliance. This work discusses the design and process blueprint for the establishment of AI pipelines that comply with the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and different sectoral standards. The aim is to offer a sound methodology to incorporate compliance checks at every stage of the AI lifecycle, from data collection to deployment. Our method focuses on incorporating privacy-preserving methods, secure data engineering principles, and auditability. Through a comparative analysis of AI development processes and regulatory requirements, we identify friction points and suggest technical as well as organizational solutions. The paper also analyzes case studies to test the proposed framework and addresses its implication for AI governance and ethical AI deployment. The research highlights the need for cross-disciplinary cooperation between legal professionals, data scientists, and organizational stakeholders to provide end-to-end compliance. We propose certain tools and technologies, including federated learning and secure multiparty computation, that enable the enforcement of regulatory requirements without undermining model effectiveness. This paper makes a contribution to current discussion by offering a guidebook to action for businesses to enact ethical AI while preserving competitive edge in a highly regulated digital environment. Keywords- AI pipeline, GDPR, HIPAA, data privacy, compliance, ethical AI, data governance, security standards, machine learning, privacy-preserving computation

The security and privacy of electronic health records (EHRs) have received considerable attention from healthcare workers and researchers. To ensure security, various encryption and decryption schemes as well as key management protocols have been developed. However, owing to sharing and scalability issues, additional security technologies have been proposed. Nonetheless, these technologies cause other problems, such as efficiency issues. Blockchain-based EHR management systems have been proposed to overcome computational overhead. However, because most blockchain systems are installed by outsourcing companies, EHRs may be leaked to the company. Hence, we herein propose a blockchain-based EHR management scheme with proxy re-encryption. In this scheme, we set a proxy server that re-encrypts the ciphertext between file servers, thereby solving EHR sharing issues. Furthermore, because the server is separated from the blockchain system, the outsourcing company cannot manipulate the server or access the records. In addition, the blockchain assists in access control by using smart contracts, thereby enabling secure and efficient EHR sharing. By performing security analysis, we prove that our proposed scheme solves the aforementioned security problems. In addition, we experimentally demonstrate the efficient operation of the proposed system.

Health data science serves as a transformative bridge between healthcare and technology, enabling data‐driven decision‐making, personalised medicine, and more effective public health interventions. This study presents a comprehensive investigation into advanced techniques such as machine learning (ML), natural language processing (NLP), predictive analytics, and data visualisation, emphasising their applications in oncology, diabetes management, radiology, cardiology, and public health. High‐quality datasets—sourced from electronic health records (EHRs), national health surveys, and clinical trial databases—were rigorously preprocessed to ensure accuracy and reliability. The interdisciplinary approach integrates expertise from computer science, statistics, biomedical engineering, and clinical medicine to foster cross‐sector collaboration. Real‐world case studies demonstrate measurable benefits, including up to a 20% improvement in early cancer detection accuracy using predictive models, a 15% reduction in diagnostic errors via AI‐assisted radiology, and enhanced personalised treatment pathways for chronic disease management. The findings underscore Health Data Science's role in evidence‐based policy‐making, illustrated by data‐driven strategies for pandemic response planning. Ethical and security considerations are addressed through compliance with the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), alongside emerging concerns over cyber risks, transparency, fairness, and accountability in AI systems. Limitations such as data integration challenges and institutional resistance are discussed, with proposed solutions. Future research directions include real‐time data processing, improved interoperability with EHR systems, and broader deployment of predictive models to enhance patient outcomes and healthcare efficiency.

Digital age data security and privacy have become vital. This is especially true in the United States, where data breaches and illegal access of private data are rising together with cybersecurity issues. This article examines how blockchain technology improves data security and privacy by using its distributed, transparent ground coupled with a cryptographic security architecture. Unlike traditional centralized databases that are vulnerable to cyberattacks and system collapse, blockchain runs on a distributed ledger system, where everyone can share information about past transactions, but no individual has the entire data set. Although there have been numerous factors shaping the development of blockchain technology over time—from its predecessors and distributed systems to early Internet cryptography developments such as cryptographic hashing, consensus mechanisms, and smart contracts—blockchain’s security-enhancing features have worked wonders in different fields such as healthcare, finance, and government operations. In healthcare, blockchain gives protection and integrity to electronic medical records. This mechanism also allows for secure data sharing between authenticated parties. In the financial sector, trading fraud protection, as well as secure transactions digitally, are more popular than ever. Through blockchain-based systems, industry-specific identity verification is now more efficient and less costly. Additionally, government departments are increasingly looking at blockchain’s role in secure voting systems, protecting citizens’ personal information, and maintaining transparent records. This article also touches on the United States’ regulatory framework, discussing everything from the Health Insurance Portability and Accountability Act (HIPAA) to the California Consumer Privacy Act (CCPA) and General Data Protection Regulation (GDPR). It demonstrates how blockchain can be used in conjunction with such an environment to guarantee compliance and data protection. While blockchain holds promise, its particularly rapid spread faces several issues. These include high costs of initial implementation, lack of scalability, and regulatory uncertainty. Many factors resist the advancement being achieved or impede what would otherwise be unheard-of expansion in underlying technology innovation and corporate accomplishments. In the end, this article emphasizes how blockchain technology transforms data security and privacy and stresses its part in building a more resilient digital infrastructure more fit for the United States. Blockchain can become a pillar for protecting data in a world growing more linked once these obstacles are solved and the creativity let flourish.

With the accelerating growth of the digital economy, data has emerged as a core asset, making secure and private data trading a pressing necessity. However, traditional centralized data trading platforms face critical challenges, including identity exposure, data leakage, unclear ownership, and lack of trust. Although decentralized, blockchain-based solutions have been proposed, they typically protect only subsets of these properties and seldom provide a unified, verifiable privacy architecture over the entire trading lifecycle. This paper introduces a novel decentralized data trading system that comprehensively integrates Groth16-based zero-knowledge proofs (ZKPs), Merkle tree–based data ownership commitments, and smart contracts on blockchain. The proposed system ensures identity anonymity, data confidentiality, ownership traceability, and behavioral privacy while supporting regulatory auditability. Rather than proposing new cryptographic primitives, we reformulate data trading as a zero-knowledge–verifiable privacy problem and embed the resulting privacy logic into the protocol and contract design. The main contributions are as follows. (1) Developing a unified zero-knowledge privacy layer that combines Groth16-based ZKPs with proxy re-encryption, allowing participants to prove transaction eligibility without disclosing identity attributes while keeping traded data encrypted end-to-end. (2) Constructing a zero-knowledge-based ownership lifecycle in which Merkle trees are repurposed as privacy-preserving ownership commitment structures that support unlinkable ownership proof, secure ownership transfer, and privacy-preserving traceability. (3) Designing a malleability-aware ZKP execution framework for Groth16 proofs, implemented via dedicated “anti-malleability” contracts that bind proofs to ownership states, fresh randomness, and protocol stages, thereby mitigating proof malleability and unsafe reuse across the registration–sale–transfer lifecycle. (4) Integrating a trusted regulatory authority into the architecture to enable compliant yet anonymous audits and formulate a system-wide privacy framework covering identity, data, ownership, behavioral, and audit dimensions. Experimental results demonstrate that the system achieves strong privacy guarantees and low on-chain overhead, offering a more robust and privacy-centric approach to data transactions than existing solutions.

Background: Patient care in the community pharmacy setting is often hindered due to limited access to adequate patient health information (PHI). Various studies suggest that lack of access to PHI is a main reason for delay in pharmaceutical care, medication dispensing errors, and lacking interprofessional relationships between prescribers and pharmacists. Literature has shown that interprofessional collaboration and improved access to PHI can improve transitions of care and communication for pharmacists, but literature is sparse on implementation of electronic health record (HER) access within independent community pharmacies. Methods: This observational study follows implementation of HER access into a rural community pharmacy to enhance common clinical services carried out by pharmacy staff. Metrics include number of enhanced consultations by pharmacy staff, type of consultations provided, potential reimbursement, decreased need to follow up with other providers, potential for decreased time to treatment or refills, and aspects of EHR most utilized during search. Results: Two-hundred sixty three patients’ profiles were assessed, with 164 (62.4%) deemed appropriate for EHR access and searching. Most interventions made were related to cardiovascular, endocrinologic, neuropsychiatric, and COVID-19 therapy medications. Conclusion: EHR access in community pharmacy has the potential to improve both the quality and availability of clinical patient interventions through enhanced knowledge of PHI.

You have accessThe ASHA LeaderBottom Line1 Sep 2011Patient Information Privacy Basics Kate RomanowJD Kate Romanow Google Scholar More articles by this author , JD https://doi.org/10.1044/leader.BML1.16092011.3 SectionsAbout ToolsAdd to favorites ShareFacebookTwitterLinked In ASHA recently hosted an online private-practice institute for audiologists and speech-language pathologists that focused on how to establish, manage, and grow a profitable private practice. Recorded lectures covered topics such as strategic business planning, fees and pricing, employment law basics, managing a fee-for-service practice, increasing referrals, using web-based and social media marketing, coding updates, Medicare billing, and claims and denials. Many of the participants in one of the sessions, “Data Privacy, Security, and Enforcement: HIPAA and More,” raised several questions during and after the institute on the Health Insurance Portability and Accountability Act (HIPAA). This article clarifies points discussed during the institute to help other clinicians understand HIPAA policies. The HIPAA privacy rule protects personal health information and outlines patients’ rights with respect to that information, while allowing disclosure of information needed for patient care. The HIPAA security rule specifies a series of administrative, physical, and technical safeguards that ensure the confidentiality, integrity, and availability of electronic protected health information. Electronic protected health information includes, for example, patient data stored on a computer hard drive, or data transmitted via a computer for billing purposes. The following questions and answers outline basic HIPAA privacy and security regulations. Q: Do the HIPAA regulations apply to me? HIPAA compliance is required by all “covered entities,” defined as health plans, health care clearinghouses, and health care providers that transmit any health information in electronic form in connection with “transactions” covered under HIPAA. If you are a provider who, for example, sends patient information electronically to a billing company, then you are a “covered entity” and must comply with HIPAA regulations. Q: What transactions are covered under HIPAA? HIPAA regulations define “transaction” as the transmission of information between two parties to carry out financial or administrative activities related to health care. It includes the following types of information transmissions: Health care claims or equivalent encounter information. Health care payment and remittance advice. Coordination of benefits. Health care claim status. Enrollment and disenrollment in a health plan. Eligibility for a health plan. Health plan premium payments. Referral certification and authorization. First report of injury. Health claims attachments. Other transactions that the secretary of health and human services may prescribe by regulation (45 C.F.R. Section 160.103). Q: What does “electronic form” mean? HIPAA does not define “electronic form.” It does, however, define “electronic media” as the following: Electronic storage media including memory devices in computers (hard drives) and any removable/transportable digital memory medium, such as magnetic tape or disk, optical disk, or digital memory card. Transmission media used to exchange information already in electronic storage media. Transmission media include, for example, the Internet, extranet (using Internet technology to link a business with information accessible only to collaborating parties), dial-up lines, leased lines, private networks, and the physical movement of removable/transportable electronic storage media. Certain transmissions, including those by paper (facsimile) and by voice (telephone), are not considered e-transmissions via electronic media because the information did not exist in electronic form before the transmission (45 C.F.R. Section 160.103). Q: If I am a covered entity, what do I need to do to comply with HIPAA? You must protect the privacy of patient information. You must safeguard patient information sent electronically. For example, you must inform patients about the HIPAA privacy practices you observe and train employees about HIPAA requirements. Q: Are there sample notices of privacy practices? More information about HIPAA privacy notices is available at the Department of Health and Human Services website. ASHA includes a sample notice of privacy practices in its Practice Management Tools for SLPs available in the ASHA online store. Q: Where I can get more information? ASHA’s reimbursement web page includes an extensive section on HIPAA. An article in The ASHA Leader also outlines some basics. The Office of Civil Rights, which enforces the privacy and security rule, has information on its website. The Workgroup for Electronic Data Interchange [PDF] (of which ASHA is a member) has information to help small practices comply with HIPAA. The full HIPAA regulations are available at the Department of Health and Human Services website. (Be aware, however, that the most current version may not be posted.) Author Notes Kate Romanow, JD, director of health care regulatory advocacy, can be reached at [email protected]. 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Health care industries are adopting Electronic Medical Record (EMR) system from paper based records. With EMR, health services can be provided at accelerated rate but privacy, security and storage of information will be of concern. There are different regulations for the use and disclosure of health information. In this research study, we focus on Health Insurance Portability and Accountability Act (HIPAA) and discuss how it protects health information of an individual while allowing the flow of information to carry out necessary health care functions. The web based EMR system is proposed based on security and privacy features defined by HIPAA.

The digitization of healthcare records and the proliferation of patient data across interconnected systems have raised significant concerns about privacy, regulatory compliance, and consent management. Traditional methods of obtaining and maintaining patient consent are often fragmented, static, and non-compliant with dynamic legal standards such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). This paper explores a blockchain-enabled framework for consent management in healthcare, focusing on enforcing patient privacy preferences and regulatory compliance. Through a comprehensive review of existing literature and frameworks, this paper proposes a decentralized consent management architecture leveraging smart contracts and distributed ledger technologies to provide secure, transparent, and tamper-proof consent enforcement. The study also outlines the potential of blockchain to automate compliance tracking, enhance interoperability, and empower patients with granular control over their health data. Recommendations for future research directions and technical challenges are also presented.

Zero Trust Architecture (ZTA) integration with Federated Learning (FL) is a game changer in authentication systems of controlled industries. This study hypothesizes the privacy saving model based on a framework that integrates behavioral biometrics with decentralized machine learning to allow continuous user authentication without accessing sensitive data. The suggested system will utilize hybrid Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) models in a federated learning setup to involve spatio-temporal trends in user behavior whilst data localization on edge devices. The outcomes of the experiment show that the accuracy rates are over 92% when it comes to identifying a user in a variety of behavioral modalities, such as the keystroke dynamics and the patterns of mouse movement. The framework is used to eliminate centralized data storage vulnerabilities that can lead to critical privacy requirements in the healthcare, financial services, and government sectors. Differential privacy mechanisms both guarantee that sensitive information is not leaked when updating the model and also guard against inference attacks with the help of secure aggregation protocols. The overhead reduction in performance measures is found to be 27% versus the traditional centralized methods. The Zero Trust model confirms user identity by behavioral signature continually giving adaptive risk scoring to access control decisions. This piece of work adds new elements of architecture combining policy enforcement points with federated aggregators and blockchain-generated audit trails. The system ensures compliance with the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) by implementing the principle of data minimum and clear processing. The viability of the framework to be deployed in the production with latency under 150ms to make authentication decisions is validated empirically in three regulated industry settings.

Healthcare providers need to communicate and exchange the clinical and administrative data from Electronic Health Records of patients. The Health Insurance Portability and Accountability Act (HIPAA) of 1996 requires the safeguarding and protection of Electronic Health Records when in transit and in storage. This paper presents a solution to protect Electronic Health Records in both states, in transit and at rest. Our secure data container guarantees data confidentiality and integrity and supports different data formats, including text and images. It can work in environments with limited Internet connectivity, which is important at times of disease pandemics or natural disasters. The potential benefit of this solution for healthcare providers would allow emailing data to patients in the form of our data container without using a HIPAA-compliant email server. This technology supports the ability to send and receive medical record documents, for a single encounter or encounters related to a condition, (1) allowing patient to download and keep their personal health record; (2) sharing continuity of care information with a provider referral to ensure medication reconciliation, for instance, with providers who are outside the information exchange network; and (3) delivering medical records securely to an insurance company who requires notes for a submitted claim. To address the problem of clinical and administrative data exchange between different healthcare providers across multiple networks, states and countries, our methodology allows patients to download and store their Electronic Health Records locally.

Click to increase image sizeClick to decrease image size Notes 1. "As a result of increasing interconnectivity, information systems and networks are now exposed to a growing number and a wider variety of threats and vulnerabilities. This raises new issues for security." OECD Guidelines for the Security of Information Systems and Networks, July 25, 2002, at p. 7, available at www.oecd.org/dataoecd/16/22/15582260.pdf. 2. Although not the subject of this article, it is important to note that countries are also enacting cybercrime legislation to make clear that certain online activities are illegal and to assist law enforcement in efforts to prosecute cyber criminals. To that end, the Privacy and Computer Crime Committee of the American Bar Association Section of Science & Technology Law has recently undertaken a project to develop a Model Cybercrime Law for the UN International Telecommunication Union's (ITU) Cybersecurity Work Programme to Assist Developing Countries. See also the International Guide to Combating Cybercrime published by this Committee, available at http://www.abanet.org/dch/committee.cfm?com=ST202003. 3. See the Appendix for a compilation of some of the key laws and regulations governing information security. 4. See, e.g., EU Data Protection Directive and HIPAA, cited in the Appendix. 5. See, e.g., E-SIGN, UETA, and UN Convention cited in the Appendix. 6. See, e.g., Kimberly Kiefer and Randy V. Sabett, Openness of Internet Creates Potential for Corporate Information Security Liability, BNA Privacy & Security Law Report, Vol. 1, No. 25 at 788 (June 24, 2002); Alan Charles Raul, Frank R. Volpe, and Gabriel S. Meyer, Liability for Computer Glitches and Online Security Lapses, BNA Electronic Commerce Law Report, Vol. 6, No. 31 at 849 (August 8, 2001); Erin Kenneally, The Byte Stops Here: Duty and Liability for Negligent Internet Security, Computer Security Journal, Vol. XVI, No. 2, 2000. 7. See, e.g., Wolfe v. MBNA America Bank, 485 F.Supp.2d 874, 882 (W.D. Tenn. 2007); Guin v. Brazos Higher Education Service, Civ. No. 05-668, 2006 U.S. Dist. Lexis 4846 (D. Minn. February 7, 2006); and Bell v. Michigan Council, 2005 Mich. App. Lexis 353 (Mich. App. February 15, 2005) (all affirming a negligence cause of action). See also, In Re TJX Companies Retail Security Breach Litigation, 2007 U.S. Dist. Lexis 77236 (D. Mass. October 12, 2007) (rejecting a negligence claim due to the economic loss doctrine, but allowing a negligent misrepresentation claim to proceed). 8. See, e.g., American Express v. Vinhnee, 2005 Bankr. Lexis 2602 (9th Cir. Bk. App. Panel, 2005); Lorraine v. Markel, 2007 U.S. Dist. Lexis 33020 (D. MD. May 4, 2007). 9. Available at www.pcisecuritystandards.org. 10. Available at www.cabforum.org. 11. ISO/IEC 27001, Information Technology—Security Techniques—Information Security Management Systems—Requirements (October 2005) (hereinafter "ISO/IEC 27001"), available for purchase at http://www.standards-online.net/InformationSecurityStandard.htm. 12. Directive 95/46/EC of the European Parliament and of the Council of October 24, 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data (hereinafter "EU Data Protection Directive"). 13. See statutes listed in the Appendix. 14. See statutes listed in the Appendix. 15. Health Insurance Portability and Accountability Act of 1996 ("HIPAA"), 42 U.S.C. 1320d-2 and 1320d-4, (providing that "each person … who maintains or transmits health information shall maintain reasonable and appropriate administrative, technical, and physical safeguards: (A) to ensure the integrity and confidentiality of the information; (B) to protect against any reasonably anticipated: (i) threats or hazards to the security or integrity of the information; and (ii) unauthorized uses or disclosures of the information; and (C) otherwise to ensure compliance with this part by the officers and employees of such person," at 42 U.S.C. 1320d-2(d)(2). 16. Gramm-Leach-Bliley Financial Services Modernization Act ("GLB"), Pub. L. No. 106-102, 113 Stat. 1338 (November 12, 1999), at §§ 501 and 505(b), 15 U.S.C. §§ 6801, 6805, providing that "[E]ach financial institution has an affirmative and continuing obligation to respect the privacy of its customers and to protect the security and confidentiality of those customers' nonpublic personal information." 17. See, Gramm-Leach-Bliley Act ("GLB"), Public Law 106-102, §§ 501 and 505(b), 15 U.S.C. §§ 6801, 6805, and implementing regulations at 12 C.F.R. Part 30, Appendix B (OCC), 12 C.F.R. Part 208, Appendix D (Federal Reserve System), 12 C.F.R. Part 364, Appendix B (FDIC), 12 C.F.R. Part 568 (Office of Thrift Supervision) and 16 C.F.R. Part 314 (FTC). 18. Final HIPAA Security Regulations, 45 C.F.R. Part 164. 19. There have also been efforts in the United States to pursue comprehensive federal privacy similar to the approach taken by many other countries. See e.g., Microsoft position paper at www.microsoft.com/presspass/download/features/2005/PrivacyLegislationCallWP.doc. Although it remains to be seen whether that approach will ultimately be adopted, it is clear that the combination of U.S. state and federal law has, in effect, imposed a comprehensive obligation of security with respect to all personal information held by all companies. 20. See, e.g., FTC enforcement actions regarding In the Matter of Sunbelt Lending Services, Inc.; In the Matter of Petco Animal Supplies, Inc.; In the Matter of MTS, Inc., d/b/a Tower records/Books/Video; In the matter of Guess?, Inc.; FTC V. Microsoft; and In the Matter of Eli Lilly and Company cited in the Appendix. 21. See, e.g., FTC enforcement actions regarding In the Matter of CardSystems Solutions, Inc.; United States v. ChoicePoint, Inc.; In the Matter of DSW Inc.; and In the Matter of BJ's Wholesale Club, Inc. cited in the Appendix. 22. See list in the Appendix. 23. See, e.g., Guin v. Brazos Higher Education Service, Civ. No. 05-668, 2006 U.S. Dist. Lexis 4846 (D. Minn. February 7, 2006) and Bell v. Michigan Council, 2005 Mich. App. Lexis 353 (Mich. App. February 15, 2005). 24. 205 Mich. App. Lexis 353 at ∗16 (Mich. App. 2005). 25. 2006 U.S. Dist. Lexis 4846 at ∗9 (D. Minn. 2006). 26. Wolfe v. MBNA America Bank, 485 F.Supp.2d 874, 882 (W.D. Tenn. 2007). 27. In Re TJX Companies Retail Security Breach Litigation, 2007 U.S. Dist. Lexis 77236 (D. Mass. October 12, 2007), at pp. 28–29. 28. Ibid. 29. The Homeland Security Act of 2002 defines the term "information system" to mean "any equipment or interconnected system or subsystems of equipment that is used in the automatic acquisition, storage, manipulation, management, movement, control, display, switching, interchange, transmission, or reception of data or information, and includes—(A) computers and computer networks; (B) ancillary equipment; (C) software, firmware, and related procedures; (D) services, including support services; and (E) related resources." Homeland Security Act of 2002, Pub. L. 107-296, at Section 1001(b), amending 44 U.S.C. § 3532(b)(4). 30. See, e.g., Australia, Information Privacy Principles under the Privacy Act 1988, Principle No. 4, available at www.privacy.gov.au/publications/ipps.html; AICPA and the Canadian Institute of Chartered Accountants (CICA), Generally Accepted Privacy principles, Principle No. 8, available at http://infotech.aicpa.org/Resources/Privacy/Generally+Accepted+Privacy+Principles; APEC, Privacy principles, Principle No. 7, available at http://austlii.edu.au/∼graham/APEC/APECv10.doc; US-EU Safe Harbor Privacy Principles, available at www.export.gov/safeharbor/SHPRINCIPLESFINAL.htm; Direct Marketing Association, Online Marketing Guidelines, available at www.the-dma.org/guidelines/onlineguidelines.shtml. 31. Directive 95/46/EC of the European Parliament and of the Council of October 24, 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data (hereinafter "EU Data Protection Directive"). 32. EU Data Protection Directive, Preamble at Para. 46. 33. EU Data Protection Directive, Article 17(1). 34. See statutes listed in the Appendix. 35. See statutes listed in the Appendix. 36. See generally, Bruce H. Nearon, Jon Stanley, Steven W. Teppler, and Joseph Burton, Life after Sarbanes-Oxley: The Merger of Information Security and Accountability, Jurimetrics Journal, Vol. 45, 379–412 (2005). 37. American Express v. Vinhnee, 336 B.R. 437; 2005 Bankr. Lexis 2602 (9th Cir. December 16, 2006). 38. Ibid., at p. 444. 39. Ibid., at p. 445. 40. Ibid., at pp. 446–447. 41. Ibid., at p. 449. 42. See, e.g., National Association of Corporate Directors, Information Security Oversight (2007). 43. Sarbanes-Oxley Act, Section 302. 44. See, e.g., GLB Security Regulations (Federal Reserve) 12 C.F.R. 208, Appendix D-2.III(A). 45. HIPAA Security Regulations, 45 C.F.R. Section 164.308(a)(2). 46. See, FTC Decisions and Consent Decrees listed in the Appendix, including Microsoft Consent Decree at II, p. 4; Ziff Davis Assurance of Discontinuance, Para. 27(a), p. 7; Eli Lilly Decision at II.A. 47. FISMA, 44 U.S.C. 3544(a). 48. E. Michael Power and Roland L. Trope, Sailing in Dangerous Waters: A Director's Guide to Data Governanc e , American Bar Association (2005), p. 13; Roland L. Trope, "Directors' Digital Fiduciary Duties," IEEE Security & Privacy, January/February 2005 at p. 78. 49. Caremark International Inc. Derivative Litigation, 698 A.2d 959 (Del. Ch. 1996). 50. Bell v. Michigan Council, 2005 Mich. App. Lexis 353 (Mich. App. February 15, 2005), at pp. 11–13 (noting that harm was foreseeable, but Board took no action). 51. Securing Cyberspace: Business Roundtable's Framework for the Future, Business Roundtable, May 19, 2004 at pp. 1, 2; available at www.businessroundtable.org/pdf//20040518000CyberSecurityPrinciples.pdf. The Business Roundtable is an association of chief executive officers of leading U.S. corporations with a combined workforce of more than 10 million employees in the United States. See www.businessroundtable.org. 52. Information Security Governance: A Call to Action, Corporate Governance Task Force Report, National Cyber Security Partnership, April 2004, pp. 12–13, available at www.cyberpartnership.org/InfoSecGov4_04.pdf. The National Cyber Security Partnership (NCSP) is led by the Business Software Alliance (BSA), the Information Technology Association of America (ITAA), TechNet, and the U.S. Chamber of Commerce in voluntary partnership with academicians, CEOs, federal government agencies, and industry experts. Following the release of the 2003 White House National Strategy to Secure Cyberspace and the National Cyber Security Summit, this public–private partnership was established to develop shared strategies and programs to better secure and enhance America's critical information infrastructure. Further information is available at www.cyberpartnership.org. 53. GLB Security Regulations (OCC), 12 C.F.R. Part 30, Appendix B, Part III.A and Part III.F. 54. See, e.g., Homeland Security Act of 2002 (Federal Information Security Management Act of 2002) 44 U.S.C. Section 3542(b)(1); GLB Security Regulations (OCC), 12 C.F.R. Part 30 Appendix B, Part II.B; HIPAA Security Regulations, 45 C.F.R. Section 164.306(a)(1); Microsoft Consent Decree at II, p. 4. 55. See, e.g., 44 USC 3532(b)(1), emphasis added. See also FISMA, 44 U.S.C. Section 3542(b)(1). Most of the foreign privacy laws also focus their security requirements from this perspective. This includes, for example, the EU Privacy Directive, Finland's Privacy Law, Italy's Privacy Law, and the UK Privacy Law. Also in this category is the Canadian Privacy Law. 56. Although they often focus on categories of security measures to address. See, e.g., HIPAA Security Regulations, 45 C.F.R. Part 164. 57. See, e.g., FDA regulations at 21 C.F.R. Part 11 (procedures and controls); SEC regulations at 17 C.F.R. 257.1(e)(3) (procedures); SEC regulations at 17 C.F.R. 240.17a-4 (controls); GLB regulations (FTC) 16 C.F.R. Part 314 (safeguards); Canada, Personal Information Protection and Electronic Documents Act, Schedule I, Section 4.7 (safeguards); EU Data Privacy Directive, Article 17(1) (measures) available at http://europa.eu.int/comm/internal_market/privacy/docs/95-46-ce/dir1995-46_part1_en.pdf. 58. See, e.g., HIPAA 42 U.S.C. 1302d-2, and HIPAA Security regulations, 45 CFR 164.306; COPPA, 15 U.S.C. 6502(b)(1)(D), and COPPA regulations 16 C.F.R. 312.8; IRS Rev. Proc. 97-22, sec. 4.01(2); SEC regulations 17 C.F.R. 257. See also UCC Article 4A, Section 202 ("commercially reasonable" security procedure), and Microsoft Consent Decree. 59. "Appropriate" security required by: HIPAA 42 U.S.C. 1302d-2, and HIPAA Security regulations, 45 CFR 164.306; EU Data Protection Directive, Article 17(1). 60. EU Data Protection Directive, Article 17(1) (emphasis added) 61. See, e.g., Belgium—Belgian Law of 8 December 1992 on Privacy Protection in relation to the Processing of Personal Data, as modified by the law of 11 December 1998 Implementing Directive 95/46/EC, and the law of 26 February 2003, Chapter IV, Article 16(4); Denmark—Act on Processing of Personal Data,; Act No. 429 of 31 May 2000 (unofficial English translation), Title IV, Part 11, Section 41(3); Estonia—Personal Data Protection Act; Passed February12, 2003 (RT1 I 2003, 26, 158), entered into force October 1, 2003, Chapter 3, Sections 19(2); Greece—Law 2472/1997 on the Protection of Individuals with regard to the Processing of Personal Data (as amended by Laws 2819/2000 and 2915/2001); Article 10(3); Ireland—Data Protection (Amendment) Act 2003; Section 2.-(1)(d) and First Schedule Article 7; Lithuania—Law on Legal Protection of Personal Data, January 21, 2003, No. IX-1296, Official translation, with amendments April 13, 2004, Article 24(1); Netherlands—25 892—Rules for the protection of personal data (Personal Data Protection Act) (Unofficial translation); Article 13; Portugal—Act on the Protection of Personal Data (transposing into the Portuguese legal system Directive 95/46/EC of the European Parliament and of the Council of October 24, 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data), Article 14(1); Slovakia—Act No 428 of July 3, 2002 on personal data protection; Section 15(1); Sweden—Personal Data Act (1998:204); issued April 29, 1998, Section 31; and UK—Data Protection Act 1998, Schedule 1, Part I, Seventh Principle. 62. See, e.g., Finland—The Finnish Personal Data Act (523/1999), given on 22.4.1999, Section 32(1); Germany—Federal Data Protection Act as of January 1, 2003, Section 9; Hungary—Act LXIII of 1992 on the Protection of Personal Data and Public Access to Data of Public Interest, Article 10(1); Italy—Personal Data Protection Code, Legislative Decree No. 196 of 30 June 2003, Sections 31 and 33; Spain—Organic Law 15/1999 of December 13 on the Protection of Personal Data, Article 9. 63. 5 USC Sec. 552a. 64. 5 U.S.C. § 552a (d)(10) (emphasis added). 65. 42 U.S.C. 1320d-2(d)(2). 66. See, Gramm-Leach-Bliley Act ("GLB"), Public Law 106-102, §§ 501 and 505(b), 15 U.S.C. §§ 6801, 6805, and implementing regulations at 12 C.F.R. Part 30, Appendix B (OCC), 12 C.F.R. Part 208, Appendix D (Federal Reserve System), 12 C.F.R. Part 364, Appendix B (FDIC), 12 C.F.R. Part 568 (Office of Thrift Supervision) and 16 C.F.R. Part 314 (FTC) (emphasis added). 67. Cal. Civil Code § 1798.81.5(b). 68. See UN Convention at Article 9(3), 9(4), and 9(5). 69. ISO/IEC 27001, Information Technology—Security Techniques—Information Security Management Systems—Requirements (October 2005). See text at footnotes 157–169, infra. February 1, 12 C.F.R. Part 30, Appendix B (OCC), 12 C.F.R. Part 208, Appendix D (Federal Reserve System), 12 C.F.R. Part 364, Appendix B (FDIC), 12 C.F.R. Part 568 (Office of Thrift May 16 C.F.R. Part 44 U.S.C. Section 45 C.F.R. 164. See of the on by of the Technology and Homeland Security of the Committee on the United States 21, 2007 at p. (noting that FTC under the GLB Act as a for the obligation to maintain reasonable and appropriate available at See also, of the the on Information and the Committee on U.S. House of on April 21, 2004, at p. 5 (noting that is an of reasonable and appropriate measures in of the available at See, e.g., FTC Decisions and Consent Decrees listed in the Appendix. See, e.g., National Association of Insurance for Information Model available at in at See, e.g., Consent Decrees listed in the Appendix. 78. See, e.g., Guin v. Brazos Higher Education Service, Civ. No. 05-668, 2006 U.S. Dist. Lexis 4846 (D. Minn. February 7, 2006) and Bell v. Michigan Council, 2005 Mich. App. 353 (Mich. App. February 15, 2005). Guin v. Brazos Higher Education Service, Civ. No. 05-668, 2006 U.S. Dist. Lexis 4846 (D. Minn. February 7, 2006). Bell v. Michigan Council, 2005 Mich. App. Lexis 353 (Mich. App. February 15, 2005). Committee on available at See, e.g., Italy—Personal Data Protection Code, Legislative Decree No. 196 of 30 June 2003, B, § Act No 428 of July 2002 on personal data § the Appendix, Act, B, Section Act, Section the Appendix, Act, Article Act, Section Act, 16(4); Act, Section 41(3); Act, Section Act, Section 32(1); Act, Section 9; Act, Article 10(3); Act, Article 10(1); Act, Article 24(1); Act, Article 13; Act, Article 14(1); Act, Section 15(1); Act, Article 9; Act, Section 31; UK Act, Schedule 1, Part I, Seventh Act, Article 7. the Appendix, Act, Schedule 2, Section Act, Act, Schedule 1, 4.7 Principle 7, Act, Section Act, Section Act, B, Sections and Act, Sections 17 and the Appendix, Act, Section A Security § Act, Section Decree the Appendix, Decree the Appendix, Act, Section Principle 4; Act, Article Act, Article Act, Sections and Act, Section Act, Section Act, Section Act, B, Sections and Act, Sections 17 and Bruce & Digital Security in a & at p. See, e.g., HIPAA Security Regulations, 45 C.F.R. Section See, e.g., Microsoft Consent Decree at II, p. 4; Ziff Davis Assurance of Discontinuance, Para. p. Eli Lilly Decision at II.B; GLB Security Regulations, 12 C.F.R. Part 30, Appendix B, Part See, e.g., Microsoft Consent Decree at II, p. 4; Eli Lilly Decision at See, e.g., FISMA, 44 U.S.C. Sections and GLB Security Regulations, 12 C.F.R. Part 30, Appendix B, Part See, e.g., In Electronic July 30, Financial Council, 2; available at See, e.g., Microsoft Consent Decree at II, p. 4; GLB Security Regulations (OCC), 12 C.F.R. Part 30 Appendix B, Part Eli Lilly Decision at II.B; HIPAA Security Regulations, 45 C.F.R. Section Information Security Management Act of 2002 44 U.S.C. Section See, e.g., Microsoft Consent Decree at II, p. 4; GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part HIPAA Security Regulations, 45 C.F.R. Section Information Security Management Act of 2002 44 U.S.C. Section See, e.g., GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part Information Security Management Act of 2002 44 U.S.C. Section See, e.g., Microsoft Consent Decree at II, p. 4; GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part See, e.g., GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part HIPAA Security Regulations, 45 C.F.R. Section See, e.g., HIPAA Security Regulations, 45 C.F.R. Section See, e.g., United States v. Cir. See, e.g., Inc. v. also v. or of to with and of See, e.g., HIPAA Security Regulations, 45 C.F.R. Section GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part and Part FISMA, 44 U.S.C. Sections and Microsoft Consent Decree at II, p. 4; Ziff Davis Assurance of HIPAA Security Regulations, 45 CFR Section and and on HIPAA Security of & Section of Science & Technology Law, No. April 2003, at p. 2, available at See, e.g., HIPAA regulations 45 C.F.R. Sections and GLB Regulations, 12 C.F.R. 208, Appendix and 12 C.F.R. Part 30, Appendix B, Part Microsoft Consent at p. 4. HIPAA Security Regulations, 45 C.F.R. Section GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part HIPAA Security Regulations, 45 C.F.R. Section HIPAA Security Regulations, 45 C.F.R. Sections and GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part HIPAA Security Regulations, 45 C.F.R. Section HIPAA Security Regulations, 45 C.F.R. Section HIPAA Security Regulations, 45 C.F.R. Section GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part HIPAA Security Regulations, 45 C.F.R. Section and Ziff Davis Assurance of Discontinuance, Para. 25, p. 6. HIPAA Security Regulations, 45 C.F.R. Section HIPAA Security Regulations, 45 C.F.R. Section HIPAA Security Regulations, 45 C.F.R. Section GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part Ziff Davis Assurance of Discontinuance, Para. p. 5 and Para. 25, p. 6. HIPAA Security Regulations, 45 C.F.R. Section GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part Ziff Davis Assurance of Discontinuance, Para. 25, p. 6. GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part Ziff Davis Assurance of Discontinuance, Para. 25, p. HIPAA Security Regulations, 45 C.F.R. Sections and Ziff Davis Assurance of Discontinuance, Para. 25, p. 6. Ziff Davis Assurance of Discontinuance, Para. 25, p. 6. HIPAA Security Regulations, 45 C.F.R. Section HIPAA Security Regulations, 45 C.F.R. Section HIPAA Security Regulations, 45 C.F.R. Section HIPAA Security Regulations, 45 C.F.R. Section HIPAA Security Regulations, 45 C.F.R. Section Ziff Davis Assurance of Discontinuance, and 26, pp. HIPAA Security Regulations, 45 C.F.R. Section GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part See, e.g., FISMA, 44 U.S.C. Section HIPAA Security Regulations, 45 C.F.R. Section Ziff Davis Assurance of Discontinuance, Para. p. 5. Ziff Davis Assurance of Discontinuance, Para. p. 7. HIPAA Security Regulations, 45 C.F.R. Section Microsoft Consent Decree at II, p. 4. FISMA, 44 U.S.C. Section Eli Lilly Decision at GLB Security Regulations, 12 C.F.R. Part 30, Appendix B, Part Ziff Davis Assurance of Discontinuance, Para. and p. 7; Eli Lilly Decision at HIPAA Security Regulations, 45 C.F.R. Section Microsoft Consent Decree at II, p. 4; Ziff Davis Assurance of Discontinuance, Para. and p. 7; Eli Lilly Decision at GLB Security Regulations, 12 C.F.R. Part 30, Appendix B, Part HIPAA Security Regulations, 45 C.F.R. Section and GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part HIPAA Security Regulations, 45 C.F.R. Section Microsoft Consent Decree at II, p. 4; Eli Lilly Decision at Microsoft Consent Decree at p. 5. Ziff Davis Assurance of Discontinuance, Para. p. 7. See, e.g., of the of the of National on 21, at See, e.g., GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part See, e.g., GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part HIPAA Security Regulations, 45 C.F.R. Section and GLB Security Regulations, 12 C.F.R. Part 30 Appendix B, Part Wolfe v. MBNA America Bank, 485 F.Supp.2d 874, 882 (W.D. Tenn. 2007). See Bell v. Michigan Council, 2005 Mich. App. Lexis 353 (Mich. App. February 15, 2005). See Guin v. Brazos Higher Education Service, Civ. No. 05-668, 2006 U.S. Dist. Lexis 4846 at (D. Minn. February 7, 2006) that a was the to and a of a was not a of a of reasonable The Financial is a of U.S. federal agencies, that the Board of of the Reserve Insurance the National the of the of the and the of Thrift on on in an Internet 8, 2006 at p. available at on on in an Internet 8, 2006 at p. available at Guide for the Guidelines Information Security December available at Information Security July available at See National Institute of and Management Guide for Information Technology No. available at ISO/IEC 27001, Information Technology—Security Techniques—Information Security Management Systems—Requirements (October 2005) (hereinafter "ISO/IEC ISO/IEC § for is the and of International and is of a of the of with and a in that the The American National Institute the United States. See, The also in and in related to including The of make its with the United States. See ISO/IEC 27001, § (emphasis added). ISO/IEC 27001, § ISO/IEC 27001, § ISO/IEC 27001, § 164. ISO/IEC 27001, § ISO/IEC 27001, §§ and 6. ISO/IEC 27001, §§ and 8. ISO/IEC 27001, §§ ISO/IEC that with an International not in from legal p. 1. EU Data Protection Directive, Article 8. Article Data Protection on the processing of personal data to health in health February 15, at pp. available at (emphasis in See list of state laws in Report, Security and Laws of 15, 2005 at Appendix available at Code, § Rev. Stat. Available at www.pcisecuritystandards.org. See list in the Appendix. See, e.g., 16 CFR Section Health Insurance Portability and Accountability Act Security Regulations, 45 C.F.R. § HIPAA security regulations to in the Act Security Regulations, 12 C.F.R. Part 30 Appendix B, Part security regulations to information in the financial Homeland Security Act of 2002 § 1001(b), amending 44 U.S.C. § and § amending 44 U.S.C. § information and information systems from unauthorized and regulations, 21 C.F.R. Part 11. See, e.g., Cal. Civil Code § 1798.81.5(b). See April 2, the Matter of of the Act of of Information and Information Services, No. No. April 2, at available at (hereinafter Wolfe v. MBNA America Bank, 485 F.Supp.2d 874, 882 (W.D. Tenn. 2007). in an Internet 12, 2005 available at This was by an on on in an Internet 8, available at of No. 25, The is an See, e.g., HIPAA Security Regulations, 45 C.F.R. § at p. 6. Ibid., at p. 3. Ibid. v. National 2007 U.S. App. Lexis 2007), at p. 13. See, e.g., on of Security Breach Personal of Privacy of April 2006 (hereinafter at pp. at note , at p. a of such in the United States and a of the number of individuals Privacy at IRS Rev. Proc. § See list of statutes in the Appendix. on for Access to Information and Part of A to Appendix, at 12 C.F.R. Part 30 (OCC), 12 C.F.R. Part (Federal Reserve System), 12 C.F.R. Part (FDIC), and 12 C.F.R. Part 568 (Office of Thrift 29, Vol. No. 29, at p. (hereinafter the maintains personal information that the not in the laws the to the or of the information, than the individuals of any of the security of the See, e.g., Code § Rev. Stat. § Code, § Stat. § Code § 2007 The also any combination of of information that to or the such as and or and See, e.g., Breach to the of & Financial Services, December 15 USC Section This that with the of at 15 USC Section and are of in this and are of in this See, and of a Duty to Information Security Computer & L. See and to Law the Protection of Personal BNA Privacy & Security Law Report, 19, p. May See at on the of the Framework for Electronic and Services, with focus on the 26, available at Science and Technology Committee, House of Internet of July 24, at Para. of the Privacy of Canada, for in to Privacy available at See Privacy Breach of the Privacy available at Privacy available at See, of the Privacy to the Law of February at available at Available at Available at to information to state

The Technology Safeguard in Health Insurance Portability and Accountability Act (HIPAA) Title II has addressed a way to maintain the integrity and non-repudiation of Electronic Medical Record (EMR). One of the important cryptographic technologies is mentioned in the ACT is digital signature; however, the ordinary digital signature (e.g. DSA, RSA, GQ...) has an inherent weakness: if the key (certificate) is updated, than all signatures, even the ones generated before the update, are no longer trustworthy. Unfortunately, the current most frequently used digital signature schemes are categorized into the ordinary digital signature scheme; therefore, the objective of this paper is to analyze the shortcoming of using ordinary digital signatures in EMR and to propose a method to use forward secure digital signature to sign EMR to ensure that the past EMR signatures remain trustworthy while the key (certificate) is updated.