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"Privacy"
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Contextual Integrity Up and Down the Data Food Chain
According to the theory of contextual integrity (CI), privacy norms prescribe information flows with reference to five parameters — sender, recipient, subject, information type, and transmission principle. Because privacy is grasped contextually (e.g., health, education, civic life, etc.), the values of these parameters range over contextually meaningful ontologies — of information types (or topics) and actors (subjects, senders, and recipients), in contextually defined capacities. As an alternative to predominant approaches to privacy, which were ineffective against novel information practices enabled by IT, CI was able both to pinpoint sources of disruption and provide grounds for either accepting or rejecting them. Mounting challenges from a burgeoning array of networked, sensor-enabled devices (IoT) and data-ravenous machine learning systems, similar in form though magnified in scope, call for renewed attention to theory. This Article introduces the metaphor of a data (food) chain to capture the nature of these challenges. With motion up the chain, where data of higher order is inferred from lower-order data, the crucial question is whether privacy norms governing lower-order data are sufficient for the inferred higher-order data. While CI has a response to this question, a greater challenge comes from data primitives, such as digital impulses of mouse clicks, motion detectors, and bare GPS coordinates, because they appear to have no meaning. Absent a semantics, they escape CI’s privacy norms entirely .
Journal Article
Challenges And Innovations In Synthetic Data Generation: Toward Context-Aware, Privacy-Preserving, And High-Utility AI Data
The dramatic increase in the number of artificial intelligence applications requires huge data sets that are balanced in terms of fidelity, privacy, and utility. Synthetic data generation has become a paramount remedy to privacy regulations, lack of data, and regulatory hurdles in the medical, financial, and autonomous domains. The classical generative models have inherent problems of distributional precision, mode collapse, privacy assurance, and computing efficiency. The Context-Aware Distribution-Adaptive Synthetic Generator framework deals with these shortcomings by jointly optimizing distributional consistency, privacy, and downstream utility. It is a combination of Wasserstein distance-based distribution matching, adaptive noise injection, covariance preservation, and hybrid GAN-VAE optimization. Context-aware caching schemes provide the opportunity of distributional modeling at fine-grained demographic, time-based, and operational segments with a guarantee of differential privacy. Experimental evaluation on standard tabular datasets shows that there are significant gains in distributional fidelity, downstream task performance, privacy preservation, and computational efficiency over standard generative methods. The framework provides building blocks to scalable, production-grade synthetic data pipelines that can be deployed to regulated, privacy-sensitive systems where optimization of many competing goals simultaneously is needed in order to have the functionality to be practically viable.
Journal Article
Privacy : a very short introduction
Some would argue that scarcely a day passes without a new assault on our privacy. In the wake of the whistle-blower Edward Snowden's revelations about the extent of surveillance conducted by the security services in the United States, Britain, and elsewhere, concerns about individual privacy have significantly increased. The Internet generates risks, unimagined even twenty years ago, to the security and integrity of information in all its forms. The manner in which information is collected, stored, exchanged, and used has changed forever; and with it, the character of the threats to individual privacy. The scale of accessible private data generated by the phenomenal growth of blogs, social media, and other contrivances of our information age pose disturbing threats to our privacy. And the hunger for gossip continues to fuel sensationalist media that frequently degrade the notion of a private domain to which we reasonably lay claim. In the new edition of this Very Short Introduction, Raymond Wacks looks at all aspects of privacy to include numerous recent changes, and considers how this fundamental value might be reconciled with competing interests such as security and freedom of expression.
Book
Three Control Views on Privacy
This paper discusses the idea that the concept of privacy should be understood in terms of control. Three different attempts to spell out this idea will be critically discussed. The conclusion will be that the Source Control View on privacy is the most promising version of the idea that privacy is to be understood in terms of control.
Journal Article
An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
The rapid advancements in technology have given rise to groundbreaking solutions and practical applications in the field of the Industrial Internet of Things (IIoT). These advancements have had a profound impact on the structures of numerous industrial organizations. The IIoT, a seamless integration of the physical and digital realms with minimal human intervention, has ushered in radical changes in the economy and modern business practices. At the heart of the IIoT lies its ability to gather and analyze vast volumes of data, which is then harnessed by artificial intelligence systems to perform intelligent tasks such as optimizing networked units’ performance, identifying and correcting errors, and implementing proactive maintenance measures. However, implementing IIoT systems is fraught with difficulties, notably in terms of security and privacy. IIoT implementations are susceptible to sophisticated security attacks at various levels of networking and communication architecture. The complex and often heterogeneous nature of these systems makes it difficult to ensure availability, confidentiality, and integrity, raising concerns about mistrust in network operations, privacy breaches, and potential loss of critical, personal, and sensitive information of the network's end-users. To address these issues, this study aims to investigate the privacy requirements of an IIoT ecosystem as outlined by industry standards. It provides a comprehensive overview of the IIoT, its advantages, disadvantages, challenges, and the imperative need for industrial privacy. The research methodology encompasses a thorough literature review to gather existing knowledge and insights on the subject. Additionally, it explores how the IIoT is transforming the manufacturing industry and enhancing industrial processes, incorporating case studies and real-world examples to illustrate its practical applications and impact. Also, the research endeavors to offer actionable recommendations on implementing privacy-enhancing measures and establishing a secure IIoT ecosystem.
Journal Article