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.

Security in Mobile Edge Caching with Reinforcement Learning

Abstract: Mobile edge computing usually uses caching to support multimedia contents in 5G mobile Internet to reduce the computing overhead and latency. Mobile edge caching (MEC) systems are vulnerable to various attacks such as denial of service attacks and rogue edge attacks. This article investigates the attack models in MEC systems, focusing on both the mobile offloading and the caching procedures. In this article, we propose security solutions that apply reinforcement learning (RL) techniques to provide secure offloading to the edge nodes against jamming attacks. We also present lightweight authentication and secure collaborative caching schemes to protect data privacy. We evaluate the performance of the RL-based security solution for mobile edge caching and discuss the challenges that need to be addressed in the future.

Privacy in the Smart City—Applications, Technologies, Challenges, and Solutions

Abstract: Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on a large number of application areas and technologies to realize complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. We therefore systematize the application areas, enabling technologies, privacy types, attackers, and data sources for the attacks, giving structure to the fuzzy term “smart city.” Based on our taxonomies, we describe existing privacy-enhancing technologies, review the state of the art in real cities around the world, and discuss promising future research directions. Our survey can serve as a reference guide, contributing to the development of privacy-friendly smart cities.

Property, Privacy, and Personal Data

Abstract: Modern computing technologies and the Internet have generated the capacity to gather, manipulate, and share massive quantities of data; this capacity, in turn, has spawned a booming trade in personal information. Even as it promises new avenues for the creation of wealth, this controversial new market also raises significant concerns for individual privacy-consumers and citizens are often unaware of, or unable to evaluate, the increasingly sophisticated methods devised to collect information about them. This Article develops a model of propertized personal information that responds to these serious concerns about privacy. It begins this task with a description and an analysis of several emerging technologies that illustrate both the promise and peril of the commodification of personal data. This Article also evaluates the arguments for and against a market in personal data, and concludes that while free alienability arguments are insufficient to justify unregulated trade in personal information, concerns about market failure and the public's interest in a protected privacy commons are equally insufficient to justify a ban on the trade. This Article develops the five critical elements of a model for propertized personal information that would help fashion a market that would respect individual privacy and help maintain a democratic order. These five elements are: limitations on an individual's right to alienate personal information; default rules that force disclosure of the terms of trade; a right of exit for participants in the market; the establishment of damages to deter market abuses; and institutions to police the personal information market and punish privacy violations. Finally, this Article returns to examples of technologies already employed in data trade and discusses how this proposed model would apply to them.

“Alexa, Can I Trust You?”

Abstract: Several recent incidents highlight significant security and privacy risks associated with intelligent virtual assistants (IVAs). Better diagnostic testing of IVA ecosystems can reveal such vulnerabilities and lead to more trustworthy systems.

Dynamic Differential Privacy for ADMM-Based Distributed Classification Learning

Abstract: Privacy-preserving distributed machine learning becomes increasingly important due to the recent rapid growth of data. This paper focuses on a class of regularized empirical risk minimization machine learning problems, and develops two methods to provide differential privacy to distributed learning algorithms over a network. We first decentralize the learning algorithm using the alternating direction method of multipliers, and propose the methods of dual variable perturbation and primal variable perturbation to provide dynamic differential privacy. The two mechanisms lead to algorithms that can provide privacy guarantees under mild conditions of the convexity and differentiability of the loss function and the regularizer. We study the performance of the algorithms, and show that the dual variable perturbation outperforms its primal counterpart. To design an optimal privacy mechanism, we analyze the fundamental tradeoff between privacy and accuracy, and provide guidelines to choose privacy parameters. Numerical experiments using customer information database are performed to corroborate the results on privacy and utility tradeoffs and design.