Abstract
The proliferation of real-time applications has motivated extensive research on analyzing and optimizing data freshness in the context of age of information. However, classical frameworks of privacy (e.g., differential privacy (DP)) have overlooked the impact of data freshness on privacy guarantees, and hence may lead to unnecessary accuracy loss when trying to achieve meaningful privacy guarantees in time-varying databases. In this work, we introduce age-dependent DP, taking into account the underlying stochastic nature of a time-varying database. In this new framework, we establish a connection between classical DP and age-dependent DP, based on which we characterize the impact of data staleness and temporal correlation on privacy guarantees. Our characterization demonstrates that aging, i.e., using stale data inputs and/or postponing the release of outputs, can be a new strategy to protect data privacy in addition to noise injection in the traditional DP framework. Furthermore, to generalize our results to a multi-query scenario, we present a sequential composition result for age-dependent DP. We then characterize and achieve the optimal tradeoffs between privacy risk and utility. Finally, case studies show that, when achieving a target of an arbitrarily small privacy risk in a single-query case, the approach of combining aging and noise injection can achieve a bounded accuracy loss, whereas using noise injection only (as in the DP benchmark) will lead to an unbounded accuracy loss.
Original language | English (US) |
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Title of host publication | SIGMETRICS/PERFORMANCE 2022 - Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems |
Publisher | Association for Computing Machinery, Inc |
Pages | 115-116 |
Number of pages | 2 |
ISBN (Electronic) | 9781450391412 |
DOIs | |
State | Published - Jun 6 2022 |
Event | 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS/PERFORMANCE 2022 - Virtual, Online, India Duration: Jun 6 2022 → Jun 10 2022 |
Publication series
Name | SIGMETRICS/PERFORMANCE 2022 - Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems |
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Conference
Conference | 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS/PERFORMANCE 2022 |
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Country/Territory | India |
City | Virtual, Online |
Period | 6/6/22 → 6/10/22 |
Funding
J. Huang is also with the Shenzhen Institute of Artificial Intelligence and Robotics for Society. This work is supported by the Zhejiang University/University of Illinois at Urbana-Champaign Institute Starting Fund, the Shenzhen Science and Technology Program (Project JCYJ20210324120011032), Guangdong Basic and Applied Basic Research Foundation (Project 2021B1515120008), the Shenzhen Institute of Artificial Intelligence and Robotics for Society, and NSF grant ECCS-2030251. Corresponding authors are Randall Berry and Jianwei Huang.
Keywords
- age of information
- differential privacy
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Networks and Communications
- Hardware and Architecture
- Software