Differentially private event sequences over infinite streams

TLDR

Continuous monitoring of traffic, disease outbreaks, and social trends relies on publishing statistics derived from sensitive user data, motivating the use of ε‑differential privacy, yet existing work scarcely addresses infinite event streams where privacy must protect entire streams. We contend that real‑world privacy risks arise from contiguous sequences of events, and therefore introduce w‑event privacy, which safeguards any sequence of w successive events in an infinite stream. We formalize w‑event privacy, motivate its relevance, and present a methodology that yields two instantiations whose utility does not depend on stream length. Experiments on real data demonstrate the practicality and effectiveness of our proposed mechanisms.

Abstract

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 ε-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.