1,045 research outputs found
Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping
Big data collection practices using Internet of Things (IoT) pervasive
technologies are often privacy-intrusive and result in surveillance, profiling,
and discriminatory actions over citizens that in turn undermine the
participation of citizens to the development of sustainable smart cities.
Nevertheless, real-time data analytics and aggregate information from IoT
devices open up tremendous opportunities for managing smart city
infrastructures. The privacy-enhancing aggregation of distributed sensor data,
such as residential energy consumption or traffic information, is the research
focus of this paper. Citizens have the option to choose their privacy level by
reducing the quality of the shared data at a cost of a lower accuracy in data
analytics services. A baseline scenario is considered in which IoT sensor data
are shared directly with an untrustworthy central aggregator. A grouping
mechanism is introduced that improves privacy by sharing data aggregated first
at a group level compared as opposed to sharing data directly to the central
aggregator. Group-level aggregation obfuscates sensor data of individuals, in a
similar fashion as differential privacy and homomorphic encryption schemes,
thus inference of privacy-sensitive information from single sensors becomes
computationally harder compared to the baseline scenario. The proposed system
is evaluated using real-world data from two smart city pilot projects. Privacy
under grouping increases, while preserving the accuracy of the baseline
scenario. Intra-group influences of privacy by one group member on the other
ones are measured and fairness on privacy is found to be maximized between
group members with similar privacy choices. Several grouping strategies are
compared. Grouping by proximity of privacy choices provides the highest privacy
gains. The implications of the strategy on the design of incentives mechanisms
are discussed
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
Techniques, Taxonomy, and Challenges of Privacy Protection in the Smart Grid
As the ease with which any data are collected and transmitted increases,
more privacy concerns arise leading to an increasing need to protect and preserve
it. Much of the recent high-profile coverage of data mishandling and public mis-
leadings about various aspects of privacy exasperates the severity. The Smart Grid
(SG) is no exception with its key characteristics aimed at supporting bi-directional
information flow between the consumer of electricity and the utility provider. What
makes the SG privacy even more challenging and intriguing is the fact that the very
success of the initiative depends on the expanded data generation, sharing, and pro-
cessing. In particular, the deployment of smart meters whereby energy consumption
information can easily be collected leads to major public hesitations about the tech-
nology. Thus, to successfully transition from the traditional Power Grid to the SG
of the future, public concerns about their privacy must be explicitly addressed and
fears must be allayed. Along these lines, this chapter introduces some of the privacy
issues and problems in the domain of the SG, develops a unique taxonomy of some
of the recently proposed privacy protecting solutions as well as some if the future
privacy challenges that must be addressed in the future.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111644/1/Uludag2015SG-privacy_book-chapter.pd
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