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.

Efficient execution of aggregation queries over encrypted relational databases

Abstract: Encryption is a common method to assure privacy of stored data. In many practical situations, decrypting data before applying logic compromises privacy. The challenge is to come up with logic transformation techniques and result-mapping methods so that the exact result of applying logic to data-in-the-clear is obtained by applying the transformed logic to encrypted data and mapping the result produced. In the scope of relational aggregation queries and in the presence of logical predicates, we show how to support needed transformations and mappings.

Louis, Lester and Pierre: three protocols for location privacy

Abstract: Location privacy is of utmost concern for location-based services. It is the property that a person's location is revealed to other entities, such as a service provider or the person's friends, only if this release is strictly necessary and authorized by the person. We study how to achieve location privacy for a service that alerts people of nearby friends. Here, location privacy guarantees that users of the service can learn a friend's location if and only if the friend is actually nearby. We introduce three protocols--Louis, Lester and Pierre--that provide location privacy for such a service. The key advantage of our protocols is that they are distributed and do not require a separate service provider that is aware of people's locations. The evaluation of our sample implementation demonstrates that the protocols are sufficiently fast to be practical.

P3P: making privacy policies more useful

Abstract: The World Wide Web Consortium's Platform for Privacy Preferences (P3P) lets Web sites convey their privacy policies in a computer-readable format. Although not yet widely adopted, P3P promises to make Web site privacy policies more accessible to users.

Federated Learning for Internet of Things: A Comprehensive Survey

Abstract: The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

Complete, safe information flow with decentralized labels

Abstract: The growing use of mobile code in downloaded applications and servlets has increased interest in robust mechanisms for ensuring privacy and secrecy. Information flow control is intended to directly address privacy and secrecy concerns, but most information flow models are too restrictive to be widely used. The decentralized label model is a new information flow model that extends traditional models with per-principal information flow policies and also permits a safe form of declassification. This paper extends this new model further, making it more flexible and expressive. We define a new formal semantics for decentralized labels and a corresponding new rule for relabeling data that is both sound and complete. We also show that these extensions preserve the ability to statically check information flow.