| Title | Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Farokhi, F. |
| Conference Name | 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS) |
| Keywords | additive noise, Ausgrid, Australian electricity distribution company, composability, data privacy, Differential privacy, evolving datasets, Human Behavior, Loss measurement, privacy, privacy loss, privacy-preserving additive noise, pubcrawl, Real-time Systems, Resiliency, Scalability, smart power grids, smart-meter measurement time-series, temporal discounting., temporally discounted differential privacy, Time measurement, time series, utility theory |
| 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 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). |
| DOI | 10.1109/ICCPS48487.2020.00008 |
| Citation Key | farokhi_temporally_2020 |
Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon