Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon
This work addresses privacy concerns for time-series data in applications like smart grids, offering a practical alternative to conventional differential privacy, though it is incremental as it builds on existing privacy frameworks.
The authors tackled the problem of ensuring privacy for evolving datasets over an infinite horizon by proposing discounted differential privacy, which uses exponential and hyperbolic discounting of privacy losses to allow bounded additive noise, and demonstrated its application on real smart-meter data.
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 across time to relax the definition of differential privacy under continual observations. This implies that privacy losses in distant past are less important than the current ones to an individual. We use discounted differential privacy to investigate privacy of evolving datasets using additive Laplace noise and show that the magnitude of the additive noise can remain bounded under discounted differential privacy. We illustrate the quality of privacy-preserving mechanisms satisfying discounted differential privacy on smart-meter measurement time-series of real households, made publicly available by Ausgrid (an Australian electricity distribution company).