Differentially Private Event Sequences over Infinite Streams

By Georgios Kellaris, Stavros Papadopoulos, Xiaokui Xiao, Dimitris Papadias. 2014

273 citations recorded by the source corpus.

Numerous applications require continuous publication of statistics or monitoring purposes, such as real-time traffic analysis, timely disease outbreak discovery, and social trends observation. These statistics may be derived from sensitive user data and, hence, necessitate privacy preservation. A notable paradigm for offering strong privacy guarantees in statistics publishing is e-differential privacy. However, there is limited literature that adapts this concept to settings where the statistics are computed over an infinite stream of "events" (i.e., data items generated by the users), and published periodically. These works aim at hiding a single event over the entire stream. We argue that, in most practical scenarios, sensitive information is revealed from multiple events occurring at contiguous time instances. Towards this end, we put forth the novel notion of w-event privacy over infinite streams, which protects any event sequence occurring in w successive time instants. We first formulate our privacy concept, motivate its importance, and introduce a methodology for achieving it. We next design two instantiations, whose utility is independent of the stream length. Finally, we confirm the practicality of our solutions experimenting with real data.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

Collecting Telemetry Data Privately (2017, background): “[24] proposes a weaker protection for continual events in w consecutive timestamps to remove the dependency on the length of time period T .”
Knock Knock, Who's There? Membership Inference on Aggregate Location Data (2017, neutral): “This is not surprising, as previous work highlights the difficulty of releasing private statistics under continual observation [6, 10, 17].”
Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, background): “In context of future research directions, few novel approaches based on the principal of differential privacy such as local privacy [337], concentrated privacy [338], w-event privacy [339], and Bayesian differential privacy [101] have very large scope in big data applications.”
Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data (2019, background): “Traditionally, many approaches tried to reach differential privacy by adding noise to the data in order to protect personal information [8,5,12], however they have never been able to provide satisfying results on real semantically-rich data; most of the implementations were limited to very specific ”
Generating synthetic data in finance: opportunities, challenges and pitfalls (2020, unknown): “[37] with associated data release algorithms.”
Differentially Private Data Publishing and Analysis: A Survey (2017, background): “[29] proposed the notion of w-event privacy to achieve a balanced privacy target.”