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.

Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures

Abstract: Smart grid privacy encompasses the privacy of information extracted by analysing smart metering data. In this paper, we suggest that home electrical power routing can be used to moderate the home's load signature in order to hide appliance usage information. In particular, 1) we introduce a power management model using a rechargeable battery, 2) we propose a power mixing algorithm, and 3) we evaluate its protection level by proposing three different privacy metrics: an information theoretic (relative entropy), a clustering classification, and a correlation/regression one; these are tested on different metering datasets. This paper sets the ground for further research on the subject of optimising home energy management with regards to hiding load signatures.

Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting

Abstract: The web has become an essential part of our society and is currently the main medium of information delivery. Billions of users browse the web on a daily basis, and there are single websites that have reached over one billion user accounts. In this environment, the ability to track users and their online habits can be very lucrative for advertising companies, yet very intrusive for the privacy of users. In this paper, we examine how web-based device fingerprinting currently works on the Internet. By analyzing the code of three popular browser-fingerprinting code providers, we reveal the techniques that allow websites to track users without the need of client-side identifiers. Among these techniques, we show how current commercial fingerprinting approaches use questionable practices, such as the circumvention of HTTP proxies to discover a user's real IP address and the installation of intrusive browser plugins. At the same time, we show how fragile the browser ecosystem is against fingerprinting through the use of novel browser-identifying techniques. With so many different vendors involved in browser development, we demonstrate how one can use diversions in the browsers' implementation to distinguish successfully not only the browser-family, but also specific major and minor versions. Browser extensions that help users spoof the user-agent of their browsers are also evaluated. We show that current commercial approaches can bypass the extensions, and, in addition, take advantage of their shortcomings by using them as additional fingerprinting features.

Analyzing Online Information Privacy Concerns: An Information Processing Theory Approach

Abstract: The advent of the Internet has made the transmission of personally identifiable information common and often inadvertent to the user. As a consequence, individuals worry that companies misuse their information. Firms have tried to mitigate this concern in two ways: (1) offering privacy policies regarding the handling and use of personal information, (2) offering benefits such as financial gains or convenience. In this paper, we interpret these actions in the context of the information processing theory of motivation. Information processing theories, in the context of motivated behavior also known as expectancy theories, are built on the premise that people process information about behavior-outcome relationships. We empirically validate predictions that the means to mitigate privacy concerns are associated with positive valences resulting in an increase in motivational score. Further, we investigate these means in trade-off situation, where a firm may only offer partially complete privacy protection and/or some benefits

Context-aware wireless sensor networks for assisted living and residential monitoring

Abstract: Improving the quality of healthcare and the prospects of "aging in place" using wireless sensor technology requires solving difficult problems in scale, energy management, data access, security, and privacy. We present AlarmNet, a novel system for assisted living and residential monitoring that uses a two-way flow of data and analysis between the front- and back-ends to enable context-aware protocols that are tailored to residents' individual patterns of living. AlarmNet integrates environmental, physiological, and activity sensors in a scalable heterogeneous architecture. The SenQ query protocol provides real-time access to data and lightweight in-network processing. Circadian activity rhythm analysis learns resident activity patterns and feeds them back into the network to aid context-aware power management and dynamic privacy policies.