Conference Proceedings

Temporally discounted differential privacy for evolving datasets on an infinite horizon

Farhad Farokhi

Proceedings - 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems, ICCPS 2020 | IEEE | Published : 2020

Abstract

We define discounted differential privacy, as an alternative to (conventional) differential privacy, to investigate privacy of evolving datasets, containing time series over an unbounded horizon. We use privacy loss as a measure of the amount of information leaked by the reports at a certain fixed time. We observe that privacy losses are weighted equally across time in the definition of differential privacy, and therefore the magnitude of privacy-preserving additive noise must grow without bound to ensure differential privacy over an infinite horizon. Motivated by the discounted utility theory within the economics literature, we use exponential and hyperbolic discounting of privacy losses ac..

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