8,326 research outputs found
Smart Meter Privacy: A Utility-Privacy Framework
End-user privacy in smart meter measurements is a well-known challenge in the
smart grid. The solutions offered thus far have been tied to specific
technologies such as batteries or assumptions on data usage. Existing solutions
have also not quantified the loss of benefit (utility) that results from any
such privacy-preserving approach. Using tools from information theory, a new
framework is presented that abstracts both the privacy and the utility
requirements of smart meter data. This leads to a novel privacy-utility
tradeoff problem with minimal assumptions that is tractable. Specifically for a
stationary Gaussian Markov model of the electricity load, it is shown that the
optimal utility-and-privacy preserving solution requires filtering out
frequency components that are low in power, and this approach appears to
encompass most of the proposed privacy approaches.Comment: Accepted for publication and presentation at the IEEE SmartGridComm.
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A Risk Management Process for Consumers
Simply by using information technology, consumers expose themselves to considerable security risks. Because no technical or legal solutions are readily available, the only remedy is to develop a risk management process for consumers, similar to the process executed by enterprises. Consumers need to consider the risks in a structured way, and take action, not once, but iteratively. Such a process is feasible: enterprises already execute such processes, and time-saving tools can support the consumer in her own process. In fact, given our society's emphasis on individual responsibilities, skills and devices, a risk management process for consumers is the logical next step in improving information security
An Android-Based Mechanism for Energy Efficient Localization Depending on Indoor/Outdoor Context
Today, there is widespread use of mobile applications that take advantage of a user\u27s location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user\u27s behalf can significantly improve a smartphone\u27s battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy
Participation Cost Estimation: Private Versus Non-Private Study
In our study, we seek to learn the real-time crowd levels at popular points
of interests based on users continually sharing their location data. We
evaluate the benefits of users sharing their location data privately and
non-privately, and show that suitable privacy-preserving mechanisms provide
incentives for user participation in a private study as compared to a
non-private study
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