Privacy Frameworks in Edge Computing

This review chapter provides an in-depth exploration of various aspects of data security, ranging from foundational principles to advanced techniques. It sheds light on the importance of maintaining confidentiality, integrity, and availability of data and presents a comprehensive overview of technologies and methodologies that can be employed to achieve robust data security. In today's digital age, ensuring the security and protection of data has become paramount. This research chapter explores various topics related to data security, including confidentiality, integrity, and availability. It also delves into the role of blockchain technology, zero-trust architecture, secure coding practices, legal and ethical considerations, homomorphic encryption, secure multi-party computation, hash functions and digital signatures, secure communication protocols, cryptocurrency and blockchain security, as well as cryptanalysis and side-channel attacks.It highlights various techniques and technologies that can be employed to protect data confidentiality, including encryption and access control mechanisms. Blockchain technology has emerged as a groundbreaking solution for enhancing data security. Zero-trust architecture challenges traditional security models by adopting a holistic and dynamic approach to data protection. Secure coding practices and DevSecOps play a vital role in developing secure software applications. The chapter investigates the importance of integrating security measures throughout the software development lifecycle. It emphasizes the adoption of secure coding practices and the implementation of DevSecOps methodologies to build resilient and secure software systems. Legal and ethical considerations are crucial when addressing data security. Homomorphic encryption and secure multi-party computation are advanced cryptographic techniques that allow data to be processed securely while encrypted. The chapter examines the principles and applications of these techniques, highlighting their significance in scenarios where privacy is paramount. Hash functions and digital signatures form the backbone of data authentication and integrity. The chapter discusses the properties and applications of hash functions and digital signatures in verifying the integrity and authenticity of data. Secure communication protocols are essential for protecting data during transmission. The chapter explores various secure communication protocols, such as Transport Layer Security (TLS), and examines their role in ensuring data confidentiality, integrity, and authenticity. Cryptocurrency and blockchain security focus on the secure storage and transfer of digital assets. The research chapter investigates the security considerations specific to cryptocurrencies and explores how block chain technology can mitigate vulnerabilities and ensure the trustworthiness of transactions. Cryptanalysis and side-channel attacks are threats to cryptographic systems. The chapter discusses these attack techniques, highlighting their potential risks and the measures that can be taken to counter them.

The integration of cloud computing into healthcare systems has revolutionized data management by enabling seamless data sharing and enhanced accessibility, thereby improving patient outcomes and operational efficiency. However, the sensitive nature of healthcare data necessitates robust privacy-preserving mechanisms to protect patient confidentiality. This paper explores state-of-the-art techniques such as cryptography, data anonymization, and secure access control, which are tailored to address privacy challenges in healthcare environments. It also critically evaluates existing frameworks, identifying limitations, and proposes a scalable architecture designed to ensure secure and efficient data sharing. Healthcare systems increasingly rely on cloud environments for storing and sharing sensitive patient data. However, these systems face significant challenges, including the risk of data breaches, unauthorized access, and the need for strict compliance with regulatory frameworks such as HIPAA and GDPR. To address these challenges, this paper presents a privacy-preserving framework that leverages advanced cryptographic methods, blockchain for secure transaction logging, and machine learning-based anomaly detection to safeguard patient data from collection to sharing. Experimental results demonstrate that the proposed system outperforms traditional methods in terms of privacy, efficiency, and scalability, showcasing its potential for real-world healthcare applications. The integration of cloud computing into healthcare systems has significantly transformed how patient data is managed and shared across multiple stakeholders, including healthcare providers, insurers, and researchers. This digital transformation has enabled real-time access to medical records, improving clinical decision-making and patient outcomes. However, the sensitive nature of healthcare data demands the adoption of advanced privacy-preserving techniques to mitigate risks such as unauthorized access, data breaches, and regulatory non-compliance. In this paper, we explore state-of-the-art technologies like cryptographic techniques (homomorphic encryption, differential privacy), data anonymization (k-anonymity, l-diversity), and access control mechanisms (RBAC, ABAC) that have been proposed to enhance data privacy and security. Additionally, we delve into the use of blockchain technology for secure, transparent transaction logging and machine learning-based anomaly detection systems to safeguard against unauthorized access. Through experimental analysis and performance testing, we demonstrate that the proposed privacy-preserving framework outperforms conventional methods in terms of scalability, efficiency, and data protection, thereby offering a promising solution for real-world healthcare applications.

The Pi-calculus has been developed to reason about behavioural equivalence. Different notions of equivalence are defined in terms of process interactions, as well as the context of processes. There are various extensions of the Pi-calculus, such as the SPI calculus, which has primitives to facilitate security protocol design.<br /> <br />Another area of computer security is access control research, which includes problems of access control models, policies and access control mechanism. The design of a unified framework for access control requires that all policies are supported and different access control models are instantiated correctly.<br /> <br />In this paper we will utilise the Pi calculus to reason about access control policies and mechanism. An equivalence of different policy implementations, as well as access control mechanism will be shown. Finally some experiences regarding the use of Pi-calculus are presented.

Cloud Computing are used to deliver services from share pool of computing resources. These resources are provided to the user using the internet. It has advantage over traditional computing services along with new challenge. These are data security, privacy, protection, access control, availability, authentication, scalability, lock-in and confidentiality. These are inherent security flaws of cloud technology due to openness, multi-tenancy, outsourcing of resources. Access control is a fundamental security mechanism but traditional access control like as mandatory, discretionary and role based access control are not suitable. These traditional existing access control model are not effective and feasible solutions for cloud. In this research work, novel access control framework is proposed that can address the security and privacy issues for cloud. The framework is based on dynamic trustworthiness of user and provides an effective and feasible access control solution for cloud. A multi layer security standard, policies and access control mechanism are provided with proposed framework. The access control is based on the trustworthiness of the user, which is demonstrated by static and dynamic trust evidence. The dynamic trustworthiness is used to reduce the possibility to perform unauthorized activities and ensures that only authorized user's access cloud resources. The prototype of the proposed framework is developed in NetLogo on Linux platform and demonstrated with test cases. The analysis of simulated results shows that proposed mechanism is highly efficient and robust under existing security threats.

The current security method of Internet-of-Vehicles (IoV) systems is rare, which makes it vulnerable to various attacks. The malicious and unauthorized nodes can easily invade the IoV systems to destroy the integrity, availability, and confidentiality of information resources shared among vehicles. Indeed, access control mechanism can remedy this. However, as a static method, it cannot timely response to these attacks. To solve this problem, we propose an intelligent edge-chain-enabled access control framework with vehicle nodes and roadside units (RSUs) in this study. In our scenario, vehicle nodes act as lightweight nodes, whereas RUSs serve as full and edge nodes to provide access control services. Considering the low accuracy of risk prediction due to limited training sets, we leverage a generative adversarial networks (GANs) to convert the risk prediction to a sequence generation. Moreover, aiming at the problems of gradient disappearance and mode collapse existed in the original GANs, we devise a Wasserstein combined GANs (WCGANs). Simulation results demonstrate that WCGAN has higher prediction accuracy than the original GANs. Additionally, it can also improve the accuracy of access control of risk prediction-based access control (RPBAC) model.

In Internet of Everything (IoE), malicious attacks detection and mitigation are important issues. These issues can be resolved through an access control framework where two entities first authenticate each other prior to establishing any secret key for their secure communication. The sensing data of various smart devices in an IoE environment are processed securely at the nearby fog servers and at the same time legitimate users can also access the real-time data directly from designated smart devices through access control mechanism. We first discuss various attack trends in an IoE environment. After that, we discuss evolution of the blockchain technology in the IoE. An artificial intelligence based blockchain-envisioned access control framework for malicious attacks detection and mitigation has been suggested to secure the IoE environment. Finally, a blockchain-based implementation has been conducted on the proposed blockchain-envisioned access control framework for measuring the computational time needed for varying number of blocks mined in the blockchain and also for varying number of transactions per block.

The exponential growth of data in recent years has created unprecedented opportunities for insights through big data analytics. However, this surge in data generation also raises significant privacy concerns, especially when sensitive information is involved. Privacy-preserving techniques have emerged as a critical area of research to ensure the ethical use of data while maintaining analytical efficacy. This paper explores state-of-the-art approaches to safeguarding privacy in big data analytics. Techniques such as data anonymization, differential privacy, secure multi-party computation, and homomorphic encryption are examined for their effectiveness and applicability across various domains. Anonymization techniques aim to remove personally identifiable information while retaining analytical utility, though challenges like re-identification attacks persist. Differential privacy introduces calibrated noise to data, balancing privacy and accuracy. Secure multi-party computation enables collaborative analytics without sharing raw data, promoting confidentiality in distributed environments. Homomorphic encryption allows computations on encrypted data, ensuring security throughout the analytical process. The integration of privacy-preserving techniques into big data workflows demands addressing computational overheads, scalability, and regulatory compliance. This paper also highlights how combining multiple privacy-preserving methods can mitigate individual limitations and enhance overall robustness. As privacy regulations evolve, such as the General Data Protection Regulation (GDPR) and similar frameworks, these techniques become increasingly vital.

The emerging networking standards such as 5G and 6G, coupled with technologies like Software Defined Networks (SDN) and Network Function Virtualization (NFV), are increasingly moving towards a multi-tenant and multi-vendor deployment model. Under these circumstances, the hardware vendors rent their networking and computation resources to multiple service providers and application developers. Such a deployment model lets various vendors collaboratively offer networking services to the tenants and the end-users at far greater efficiency and better affordability. However, the issues around trust, ownership, and data security become a concern for tenants and vendors in such multi-tenant and multi-vendor setting. In particular, the centralized nature of SDN controllers, together with the limitations of the contemporary authentication and access control mechanisms, make multi-stakeholder SDN deployments susceptible to several Sybil and trust-related exploits. We present EDISON, a blockchain-based authentication and access control framework, for multi-stakeholder SDN infrastructure that adheres to the Zero-trust security model. It allows the network vendors and third-party service providers to securely set up a service-level agreement while enabling the concerned stakeholders to audit the network operations through an end-to-end encrypted tamper-proof ledger. EDISON creates an ecosystem structured on smart contracts, wherein the network elements rented and used by the tenants interact with the services deployed in the form of contracts to enable decentralized and transparent orchestration.

It is our great pleasure to welcome you to the 10th annual Workshop on Encrypted Computing and Applied Homomorphic Cryptography - WAHC'22. WAHC was created in 2013 as a forum to organize and foster discussion of a wide variety of aspects of encrypted computing and secure computation. In a world where distance is no longer an obstacle for cooperation, secure computation is becoming a key feature of current and future information systems. Distributed network applications and cloud architectures are at danger because lots of personal consumer data is aggregated in all kinds of formats and for various purposes. Industry and consumer electronics companies are facing massive threats like theft of intellectual property and industrial espionage. Public infrastructure has to be secured against sabotage and manipulation. A possible solution is encrypted computing: Data can be processed on remote, possibly insecure resources, while program code and data is encrypted all the time. This allows to outsource the computation of confidential information independently from the trustworthiness or the security level of the remote system. The goal of the workshop is to bring together researchers with practitioners and industry to present, discuss and to share the latest progress in the field. We want to exchange ideas that address real-world problems with practical approaches and solutions. The workshop is typically uniformly attended by academia, government, and industry, with attendees both from prior years with experience in the domain and new attendees learning from the community. Specific encrypted computing technologies focus on homomorphic encryption and secure multiparty computation but also need to interoperate with existing technologies and infrastructures. The technologies and techniques discussed in this workshop are key to extending the range of applications that can be securely and practically outsourced.

The healthcare industry faces significant challenges in safeguarding data security and integrity, particularly with the rise of digital health systems and the sensitive nature of patient information. Blockchain technology offers a decentralized, immutable, and secure solution to address these issues, allowing healthcare organizations to protect and manage patient data effectively. This paper examines how blockchain can enhance data security and integrity within healthcare by examining its key features, such as immutability, transparency, and decentralized access, which collectively provide a robust framework for secure data management. The potential applications of blockchain in healthcare, including patient consent management, access control through smart contracts, and data-sharing solutions, are analyzed alongside challenges such as regulatory compliance, cost, and interoperability. By integrating blockchain, healthcare systems can achieve improved data security, fostering trust and resilience in patient data management. This paper concludes with case studies and discusses future trends in blockchain implementation within healthcare, underscoring its transformative potential for securing data while addressing privacy and ethical concerns. Keywords: Blockchain, healthcare, data security, data integrity, patient data management, smart contracts.

A framework and risk assessment approaches for risk-based access control in the cloud

With the development of Internet-of-Things (IoT) technology, various types of information, such as social resources and physical resources, are deeply integrated for different comprehensive applications. Social networking, car networking, medical services, video surveillance, and other forms of the IoT information service model gradually change people’s daily lives. Facing the vast amounts of IoT information data, the IoT search technology is used to quickly find accurate information to meet the real-time search needs of users. However, IoT search requires using a large amount of user private information, such as personal health information, location information, and social relations information, to provide personalized services. Employing private information from users will encounter security problems if an effective access control mechanism is missing during the IoT search process. An access control mechanism can effectively monitor the access activities of resources and ensure that authorized users access information resources under legitimate conditions. This survey examines the growing literature on access control for an IoT search. Problems and challenges of access control mechanisms are analyzed to facilitate the adoption of access control solutions in real-life settings. This article aims to provide theoretical, methodological, and technical guidance for IoT search access control mechanisms in large-scale dynamic heterogeneous environments. Based on a literature review, we also analyzed the future development direction of access control in the age of IoT.

The rapid adoption of cloud computing has transformed the way data is stored, shared, and accessed across organizationsand individuals. Despite its scalability and cost efficiency, cloud storage introduces serious security and privacy challenges, particularly when sensitive data is outsourced to third-party cloud service providers. Traditional access control mechanisms often rely on the cloud provider’s trustworthiness, which may expose data to unauthorized access, insider threats, and data breaches. To address these concerns, CryptCloud+ is proposed as a secure and expressive data access control framework that enforces fine-grained authorization directly through cryptographic techniques. CryptCloud+ focuses on ensuring data confidentiality while enabling flexible and policy-driven access control over encrypted cloud data. The system leverages encryption-based access control, where data is encrypted before being uploaded to the cloud, and only authorized users possessing valid cryptographic credentials can decrypt and access the data. This approach eliminates the need to trust the cloud service provider, as all sensitive operations related to data access are handled at the client side. A key contribution of CryptCloud+ is its support for expressive access policies, allowing data owners to define complex rules based on user attributes such as roles, departments, access levels, or time constraints. These policies enable fine grained access control without requiring data owners to manage individual permissions manually. The framework ensures that users can access only the data they are authorized for, while unauthorized users, including cloud administrators, are unable to infer any meaningful information from the encrypted data. The proposed system is implemented using Python, taking advantage of its robust cryptographic libraries, simplicity, and integration capabilities. Python-based modules handle key generation, encryption, decryption, and policy enforcement, making the system modular and easy to extend. The implementation demonstrates how secure key management and attribute-based access enforcement can be achieved efficiently in a real-world cloud environment. CryptCloud+ also addresses scalability and performance concerns by minimizing computation overhead during data access. Encryption and decryption operations are optimized to ensure that system performance remains practical even as the number of users and data files increases. Experimental results indicate that the framework maintains strong security guarantees while providing acceptable response times for authorized users. In addition to security, the system enhances usability by allowing seamless data sharing among multiple users without re encrypting data for each individual. This significantly reduces management complexity for data owners while preserving strict access control policies. The system design ensures compatibility with existing cloud storage services, making itadaptable to various deployment scenarios. Overall, CryptCloud+ presents a secure, flexible, and effiient solution for cloud data access control. By combining cryptographic enforcement with expressive policy definition and Python-based implementation, the frameworkstrengthens data security, protects user privacy, and improves trust in cloud storage systems. The proposed model demonstrates the feasibility of practical, cryptography-driven access control for modern cloud environments.

The prevailing trend of the seamless digital collection has prompted privacy concern not only among academia but also among the majority. In enforcing the automation of privacy policies and law, access control has been one of the most devoted subjects. Despite the recent advances in access control frameworks and models, there are still issues that impede the development of effective access control. Among them are the lack of assessment’s granularity in user authorization, and reliance on identity, role or purpose-based access control schemes. In this paper, we address the problem of protecting sensitive attributes from inappropriate access. We propose an access control mechanism that employs two trust metrics name experience and behavior. We also propose a scheme for quantifying those metrics in an enterprise computing environment. Finally, we show that these metrics are useful in improving the assessment granularity in permitting or prohibiting users to gain access to sensitive attributes.

In the age of IoT, access control of smart objects and services has become more important than ever. However, existing access control mechanisms are not flexible enough to cover the demands in many of the real use cases in IoT scenarios. For instance, visitors of IoT-enabled buildings may need to use the IoT facilities of the room they visit, for a limited period of time. In order to realize such flexibility in access control of IoT, we propose AFWA, an access control framework that seamlessly integrates Web API in access control. By integrating Web API in access control, event information from Calendar API, for example, can be used for judging access permissions of IoT devices.