6,887 research outputs found
Fault-Tolerant Secure Data Aggregation Schemes in Smart Grids: Techniques, Design Challenges, and Future Trends
Secure data aggregation is an important process that enables a smart meter to perform efficiently and accurately. However, the fault tolerance and privacy of the user data are the most serious concerns in this process. While the security issues of Smart Grids are extensively studied, these two issues have been ignored so far. Therefore, in this paper, we present a comprehensive survey of fault-tolerant and differential privacy schemes for the Smart Gird. We selected papers from 2010 to 2021 and studied the schemes that are specifically related to fault tolerance and differential privacy. We divided all existing schemes based on the security properties, performance evaluation, and security attacks. We provide a comparative analysis for each scheme based on the cryptographic approach used. One of the drawbacks of existing surveys on the Smart Grid is that they have not discussed fault tolerance and differential privacy as a major area and consider them only as a part of privacy preservation schemes. On the basis of our work, we identified further research areas that can be explored
Privacy-Friendly Mobility Analytics using Aggregate Location Data
Location data can be extremely useful to study commuting patterns and
disruptions, as well as to predict real-time traffic volumes. At the same time,
however, the fine-grained collection of user locations raises serious privacy
concerns, as this can reveal sensitive information about the users, such as,
life style, political and religious inclinations, or even identities. In this
paper, we study the feasibility of crowd-sourced mobility analytics over
aggregate location information: users periodically report their location, using
a privacy-preserving aggregation protocol, so that the server can only recover
aggregates -- i.e., how many, but not which, users are in a region at a given
time. We experiment with real-world mobility datasets obtained from the
Transport For London authority and the San Francisco Cabs network, and present
a novel methodology based on time series modeling that is geared to forecast
traffic volumes in regions of interest and to detect mobility anomalies in
them. In the presence of anomalies, we also make enhanced traffic volume
predictions by feeding our model with additional information from correlated
regions. Finally, we present and evaluate a mobile app prototype, called
Mobility Data Donors (MDD), in terms of computation, communication, and energy
overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201
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