Continual Counting with Gradual Privacy Expiration
Joel Daniel Andersson, Monika Henzinger, Rasmus Pagh, Teresa Anna, Steiner, Jalaj Upadhyay

TL;DR
This paper investigates continual counting under differential privacy with gradual privacy expiration, providing algorithms with tight bounds on error and demonstrating improved empirical privacy loss over baseline methods.
Contribution
It introduces new bounds and algorithms for continual counting with gradual privacy expiration, closing the gap between upper and lower bounds for certain expiration functions.
Findings
Achieves $O(rac{ ext{log}(T)}{ ext{epsilon}})$ error for many expiration functions.
Provides a lower bound linking error and privacy expiration function.
Empirical results show reduced privacy loss compared to baseline algorithms.
Abstract
Differential privacy with gradual expiration models the setting where data items arrive in a stream and at a given time the privacy loss guaranteed for a data item seen at time is , where is a monotonically non-decreasing function. We study the fundamental problem where each data item consists of a bit, and the algorithm needs to output at each time step the sum of all the bits streamed so far. For a stream of length and privacy expiration continual counting is possible with maximum (over all time steps) additive error and the best known lower bound is ; closing this gap is a challenging open problem. We show that the situation is very different for privacy with gradual expiration by giving upper and lower bounds for a large set of…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Internet Traffic Analysis and Secure E-voting
