Information privacy

About: Information privacy is a research topic. Over the lifetime, 25412 publications have been published within this topic receiving 579611 citations. The topic is also known as: data privacy & data protection.

Protecting Privacy in an Information Age: The Problem of Privacy in Public

Abstract: Philosophical and legal theories of privacy have long recognized the relationship between privacy and information about persons. They have, however, focused on personal, intimate, and sensitive information, assuming that with public information, and information drawn from public spheres, either privacy norms do not apply, or applying privacy norms is so burdensome as to be morally and legally unjustifiable. Against this preponderant view, I argue that information and communications technology, by facilitating surveillance, by vastly enhancing the collection, storage, and analysis of information, by enabling profiling, data mining and aggregation, has significantly altered the meaning of public information. As a result, a satisfactory legal and philosophical understanding of a right to privacy, capable of protecting the important values at stake in protecting privacy, must incorporate, in addition to traditional aspects of privacy, a degree of protection for privacy in public.

Protecting Privacy in an Information Age: The Problem of Privacy in Public

Abstract: Philosophical and legal theories of privacy have long recognized the relationship between privacy and information about persons. They have, however, focused on personal, intimate, and sensitive information, assuming that with public information, and information drawn from public spheres, either privacy norms do not apply, or applying privacy norms is so burdensome as to be morally and legally unjustifiable. Against this preponderant view, I argue that information and communications technology, by facilitating surveillance, by vastly enhancing the collection, storage, and analysis of information, by enabling profiling, data mining and aggregation, has significantly altered the meaning of public information. As a result, a satisfactory legal and philosophical understanding of a right to privacy, capable of protecting the important values at stake in protecting privacy, must incorporate, in addition to traditional aspects of privacy, a degree of protection for privacy in public.

A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

Abstract: Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An emerging model, called federated learning (FL), is rising above both centralized systems and on-site analysis, to be a new fashioned design for ML implementation. It is a privacy-preserving decentralized approach, which keeps raw data on devices and involves local ML training while eliminating data communication overhead. A federation of the learned and shared models is then performed on a central server to aggregate and share the built knowledge among participants. This article starts by examining and comparing different ML-based deployment architectures, followed by in-depth and in-breadth investigation on FL. Compared to the existing reviews in the field, we provide in this survey a new classification of FL topics and research fields based on thorough analysis of the main technical challenges and current related work. In this context, we elaborate comprehensive taxonomies covering various challenging aspects, contributions, and trends in the literature, including core system models and designs, application areas, privacy and security, and resource management. Furthermore, we discuss important challenges and open research directions toward more robust FL systems.

Trading Private Range Counting over Big IoT Data

Abstract: Data privacy arises as one of the most important concerns, facing the pervasive commoditization of big data statistic analysis in Internet of Things (IoT). Current solutions are incapable to thoroughly solve the privacy issues on data pricing and guarantee the utility of statistic outputs. Therefore, this paper studies the problem of trading private statistic results for IoT data, by considering three factors. Specifically, a novel framework for trading range counting results is proposed. The framework applies a sampling-based method to generate approximated counting results, which are further perturbed for privacy concerns and then released. The results are theoretically proved to achieve unbiasedness, bounded variance, and strengthened privacy guarantee under differential privacy. Moreover, a pricing approach is proposed for the traded results, which is proved to be immune against arbitrage attacks. The framework is evaluated by estimating the air pollution levels with different ranges on 2014 CityPulse Smart City datasets. The analysis and evaluation results demonstrate that our framework greatly reduces the error of range counting approximation; and the optimal perturbation approach enables that the private counting satisfies the specified approximation degree while providing strong privacy guarantee.

Purpose based access control for privacy protection in relational database systems

Abstract: In this article, we present a comprehensive approach for privacy preserving access control based on the notion of purpose. In our model, purpose information associated with a given data element specifies the intended use of the data element. A key feature of our model is that it allows multiple purposes to be associated with each data element and also supports explicit prohibitions, thus allowing privacy officers to specify that some data should not be used for certain purposes. An important issue addressed in this article is the granularity of data labeling, i.e., the units of data with which purposes can be associated. We address this issue in the context of relational databases and propose four different labeling schemes, each providing a different granularity. We also propose an approach to represent purpose information, which results in low storage overhead, and we exploit query modification techniques to support access control based on purpose information. Another contribution of our work is that we address the problem of how to determine the purpose for which certain data are accessed by a given user. Our proposed solution relies on role-based access control (RBAC) models as well as the notion of conditional role which is based on the notions of role attribute and system attribute.