Application-Oriented Selection of Privacy Enhancing Technologies

Journal Article The trouble with Article 25 (and how to fix it): the future of data protection by design and default Get access Ira S Rubinstein, Ira S Rubinstein Nathaniel Good, Good Research, LLC/UC Berkeley School of Information Ira S. Rubinstein, NYU School of Law. Email: ira.rubinstein@nyu.edu Search for other works by this author on: Oxford Academic Google Scholar Nathaniel Good Nathaniel Good Nathaniel Good, Good Research, LLC/UC Berkeley School of Information Search for other works by this author on: Oxford Academic Google Scholar International Data Privacy Law, Volume 10, Issue 1, February 2020, Pages 37–56, https://doi.org/10.1093/idpl/ipz019 Published: 25 October 2019 Article history Received: 24 April 2019 Revision received: 05 August 2019 Accepted: 01 October 2019 Published: 25 October 2019

In the era heavily influenced by Internet of Things (IoT), prioritizing strong security and the protection of user privacy is of utmost importance. This comprehensive review paper embarks on a meticulous examination of the multifaceted challenges and risks facing IoT security and privacy. It encompasses the hardware, software, and data‐in‐transit domains, shedding light on potential vulnerabilities and associated threats. In response to these concerns, this paper puts forth recommendations for effective strategies to mitigate these risks. Providing a road‐map for enhancing security and privacy in IoT environments. Furthermore, this review thoroughly assesses a multitude of solutions proposed by various authors, with the primary aim of enhancing security and privacy within the IoT landscape. The analysis provides insights into the strengths and limitations of these solutions. This is aiding in the development of a holistic comprehension of the existing status of IoT security and privacy. Moreover, the paper delves into the complexities surrounding integrating emerging technologies into the IoT framework. It explores the obstacles and challenges inherent in this process and proposes potential strategies to address these hurdles. By doing so, the review provides a holistic perspective on existing security and privacy enhancement technologies and offers guidance on navigating the dynamic landscape of emerging technologies within the IoT domain. Publications included in the review consist of journal articles, conference papers, and book chapters from reputable sources indexed in SCI (Science Citation Index), Scopus, and Web of Science.

FIPs and PETs for RFID: Protecting Privacy in the Web of Radio Frequency Identification

Private data should often not distributed to other users than those that got an access on common consent and settled trust. Nobody and no company else should have an access to such data. Protection of privacy becomes more difficult in mobile environments and thus requires novel approaches to privacy enhancing technologies. The Privacy Wallet is a mobile application that supports exchange of data on consent and protects from unauthorised access and distribution. The owner of the data is the master of that data. The holder has only access on common consent.

In this work we present a multibiometric face recognition framework based on combining information from 2D with 3D facial features. The 3D biometrics channel is protected by a privacy enhancing technology, which uses error correcting codes and cryptographic primitives to safeguard the privacy of the users of the biometric system at the same time enabling accurate matching through fusion with 2D. Experiments are conducted to compare the matching performance of such multibiometric systems with the individual biometric channels working alone and with unprotected multibiometric systems. The results show that the proposed hybrid system incorporating template protection, match and in some cases exceed the performance of corresponding unprotected equivalents, in addition to offering the additional privacy protection.

The rapid expansion of the digital economy and the exponential growth of data transmitted across national borders have made the harmonisation of personal data protection regimes a central issue in contemporary international law. Using comparative legal analysis, this article examines international, regional, and national legal instruments governing cross-border transfers of personal data. These include the Organisation for Economic Co-operation and Development Guidelines on the Protection of Privacy and Transborder Flows of Personal Data, the Council of Europe Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data, as modernised, the European Union General Data Protection Regulation and its recent adequacy decision concerning the United States, the Asia-Pacific Economic Cooperation and Global Cross-Border Privacy Rules systems, the Organisation for Economic Co-operation and Development Declaration on Government Access to Personal Data, United States national security regulations adopted under Executive Order No. 14117, China’s simplified 2024 rules on cross-border data transfers, Brazil’s 2024 regulation on international data transfers, and Russia’s 2025 amendments on data localisation. The article evaluates trust-building mechanisms, including standard contractual clauses, binding corporate rules, certification mechanisms, and privacy-enhancing technologies, and assesses the Group of Twenty initiative on Data Free Flow with Trust and related trade policy recommendations aimed at reducing regulatory fragmentation. The study concludes that, in the absence of a universal international treaty, mutual recognition of safeguards and the widespread use of privacy-by-design technologies are essential to balancing the protection of human rights with the promotion of innovation.

This article explores the application of homomorphic encryption (HE) in secure ad targeting, addressing the critical challenge of balancing personalized advertising with user privacy concerns in the digital advertising ecosystem. We examine the fundamentals of HE, its integration into ad targeting processes, and propose a privacy-preserving ad platform architecture. Through a comprehensive feasibility analysis and performance evaluation, we assess the technical challenges, computational overhead, and scalability issues associated with implementing HE in real-time ad serving. Our findings indicate that while HE offers strong privacy guarantees, it currently faces limitations in terms of latency and throughput compared to traditional ad targeting methods. We analyze the trade-offs between privacy protection and targeting effectiveness, highlighting the impact on ad relevance and personalization. The article also discusses future directions, including advancements in HE algorithms, integration with other privacy-enhancing technologies, and regulatory considerations. By synthesizing current research and experimental results, this work provides valuable insights into the potential of HE to revolutionize privacy-preserving ad targeting, paving the way for a more secure and privacy-conscious digital advertising future.

IntroductionThe integration of artificial intelligence (AI) into medical text generation is transforming public health by enhancing clinical documentation, patient education, and decision support. However, the widespread deployment of AI in this domain introduces significant ethical challenges, including fairness, privacy protection, and accountability. Traditional AI-driven medical text generation models often inherit biases from training data, resulting in disparities in healthcare communication across different demographic groups. Moreover, ensuring patient data confidentiality while maintaining transparency in AI-generated content remains a critical concern. Existing approaches either lack robust bias mitigation mechanisms or fail to provide interpretable and privacy-preserving outputs, compromising ethical compliance and regulatory adherence.MethodsTo address these challenges, this paper proposes an innovative framework that combines privacy-preserving AI techniques with interpretable model architectures to achieve ethical compliance in medical text generation. The method employs a hybrid approach that integrates knowledge-based reasoning with deep learning, ensuring both accuracy and transparency. Privacy-enhancing technologies, such as homomorphic encryption and secure multi-party computation, are incorporated to safeguard sensitive medical data throughout the text generation process. Fairness-aware training protocols are introduced to mitigate biases in generated content and enhance trustworthiness.Results and discussionThe proposed approach effectively addresses critical challenges of bias, privacy, and interpretability in medical text generation. By combining symbolic reasoning with data-driven learning and embedding ethical principles at the system design level, the framework ensures regulatory alignment and improves public trust. This methodology lays the groundwork for broader deployment of ethically sound AI systems in healthcare communication.

Several opinion polls have reported that many people claim to be concerned about their privacy, yet that most people in fact do very little to protect their privacy. Are privacy concerns indeed insufficient motivators to adopt privacy protection strategies? What then characterizes the users of these strategies? On the basis of a large scale survey amongst Dutch students, this paper explores the relation between privacy risk perception and privacy protection strategies in more detail. It elaborates on factors that constitute privacy risk perception, as well as three kinds of strategies adopted by individuals to protect their privacy: behavioral measures, common privacy enhancing technologies (PETs), and more complex PETs. Next, it explores the relation between the respondents’ perception and the strategies they employ in more detail to answer the question what characteristics the users of the various strategies have in terms of perception, gender and age. Gender appears not to influence privacy risk perception, yet men are more familiar with the various privacy protection strategies and use them more of-ten than women. In general, a higher privacy risk perception does not lead to the adoption of stronger or more protection strategies, except for the use of pseudonyms, cookie crunchers, anonymous email, safe email, and providing false personal data. Our analysis deepens the understanding of privacy risk perception and privacy protection strategies, yet leaves the privacy paradox unresolved.

Privacy as a Non-Price Competition Parameter: Theories of Harm in Mergers

In personalised search, user information needs captured through cookies and Web search history for example, make it possible to infer personal or sensitive information about a person. Although prior studies have established sources of privacy leakage on the Web, there is a need for identifying the sources of data leakage concerning personalised search, its impact on users and on the broader privacy laws and regulations. This research study firstly explores the significance of attributes impacting personalised search and considers whether the extensive collection of personal data is necessary for personalised search results through a series of experiments measuring the impact of personalisation and sources of data leakage. These findings will then be evaluated two-fold: through a qualitative study of users, and assessed for its applicability in the Australian context as per the Australian Privacy Principles. Further, the outcomes from the experimental and user studies will be used to develop a Privacy-Enhancing Technology (PET) that will provide users with options to control personal data leakage whilst searching on the Web and enable proactive protection of individual user privacy.

Non-functional Requirements (NFRs) such as software quality attributes, software design constraints and software interface requirements hold crucial information about the constraints on the software system under development and its behavior. NFRs are subjective in nature and have a broad impact on the system as a whole. Being distinct from Functional Requirements (FR), NFRs are dealt with special attention, as they play an integral role during software modeling and development. However, since Software Requirements Specification (SRS) documents, in practice, are written in natural language, solely holding the perspectives of the clients, the documents often end up with FR and NFR statements mixed together in the same paragraphs. It is, therefore, left upon the software analysts to classify and separate them manually. The research, presented in this paper, aims to automate the process of detecting NFR sentences by using a text classifier equipped with a part-of-speech (POS) tagger. The results reported in this paper outperform the recent work in the field, and achieved a higher accuracy of 98.56% using 10-folds-cross-validation over the same data used in the literature. The research reported in this paper is part of a larger project aimed at applying Natural Language Processing techniques in Software Requirements Engineering.KeywordsRequirement EngineerCutoff ThresholdLinguistic KnowledgeRequirement ElicitationSyntactic FeatureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

With the growing use of UML diagrams for software architecture description and the importance of non-functional requirements evaluation at software architecture level, filling the scientific gap between architect and requirement analyst is considered to be significant. Software architects are not usually familiar with non-functional requirement analysis and are not able to analyze such requirements easily. On the other hand, non-functional requirements cannot be evaluated directly by UML diagrams. Therefore, the architect should annotate additional information of the nonfunctional requirements to software architecture description and then an executable model can be produced. These executable models can be petri nets, queuing networks, stochastic process algebra and etc. One kind of the UML diagrams that can be used to describe software architecture is component diagram. In this paper, we propose a new algorithm that enables an architect to transform a component diagram into an executable model based on different extensions of petri nets. Moreover, we show how to use this petri net model for performance evaluation and simulation and the implications of this transformation are described completely. Finally, to represent the usage of our proposed algorithm, we consider a case study as an example.

The satisfaction of Non-Functional Requirements (NFRs) has a clear impact in the success of Ambient Intelligence (AmI) Systems. However, existing methods and techniques to engineer these systems lack support in their specific and systematic treatment. In this paper, a Model-Driven Engineering (MDE) method for the design of AmI systems is introduced to deal with NFRs paying special attention to those particularly related to the features of these kind of systems, namely, unobtrusiveness, context-awareness, and self-adaptativeness. The aim is to enable the semi-automatic derivation of quality software design models from an NFRs specification of the system, with special focus on agent-oriented architectures. To this end, Computation Independent Models (CIM) and transformation techniques between them are defined, as well as an evaluation procedure to be applied, which can help developers to make successful solution decisions satisficing the overall quality properties required for AmI systems, and also specific ones for each particular system. The proposal is illustrated by means of a case study of an Ambient Assisted Living (AAL) system for elderly people.

With the growing use of UML diagrams for software design description and the importance of nonfunctional requirements evaluation at software development process, transforming these diagrams to executable models is considered to be significant. Nonfunctional requirements can not be evaluated directly by UML diagrams. Software designers are not usually familiar with non-functional requirement analysis and are not able to analyze such requirements easily. Therefore the designer should produce an executable model from software design description to be ready for analysis. usecase and sequence diagrams are the most important UML diagrams for software design description. In this paper, we propose new algorithms that enable a designer to transform usecase and sequence diagrams into executable models based on Petri nets and then we show how to use this Petri net models for simulation. Finally, to represent the usage of our proposed algorithms, we consider a case study as an example.