# Active Privacy-Utility Trade-off Against Inference in Time-Series Data Sharing

arXiv: https://arxiv.org/abs/2202.05833 · HTML: https://mlsanity.com/paper/2202.05833/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 65 references that are in the map.
- First posted on arXiv 11 Feb 2022.

## Builds on (most cited first, 11 of 11)

- [Privacy-Preserving Adversarial Networks](https://mlsanity.com/paper/1712.07008/) (2017)
- [Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.02685/) (2020)
- [On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms](https://mlsanity.com/paper/1605.02277/) (2016)
- [Quantifying Differential Privacy under Temporal Correlations](https://mlsanity.com/paper/1610.07543/) (2016)
- [Privacy-Aware Communication Over a Wiretap Channel with Generative Networks](https://mlsanity.com/paper/2110.04094/) (2021)
- [Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary](https://mlsanity.com/paper/2102.08308/) (2021)
- [Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations](https://mlsanity.com/paper/1711.11436/) (2017)
- [Composition Properties of Inferential Privacy for Time-Series Data](https://mlsanity.com/paper/1707.02702/) (2017)
- [Privacy-Aware Location Sharing with Deep Reinforcement Learning](https://mlsanity.com/paper/1907.07606/) (2019)
- [Privacy Against Brute-Force Inference Attacks](https://mlsanity.com/paper/1902.00329/) (2019)
- [Smart Meter Privacy with Renewable Energy and a Finite Capacity Battery](https://mlsanity.com/paper/1605.04814/) (2016)

## Built on by (most cited first, 1 of 1)

- [Evasive Active Hypothesis Testing with Deep Neuroevolution: The Single- and Multi-Agent Cases](https://mlsanity.com/paper/2403.10112/) (2024)

## Abstract (arXiv)

Internet of things (IoT) devices, such as smart meters, smart speakers and activity monitors, have become highly popular thanks to the services they offer. However, in addition to their many benefits, they raise privacy concerns since they share fine-grained time-series user data with untrusted third parties. In this work, we consider a user releasing her data containing personal information in return of a service from an honest-but-curious service provider (SP). We model user's personal information as two correlated random variables (r.v.'s), one of them, called the secret variable, is to be kept private, while the other, called the useful variable, is to be disclosed for utility. We consider active sequential data release, where at each time step the user chooses from among a finite set of release mechanisms, each revealing some information about the user's personal information, i.e., the true values of the r.v.'s, albeit with different statistics. The user manages data release in an online fashion such that the maximum amount of information is revealed about the latent useful variable as quickly as possible, while the confidence for the sensitive variable is kept below a predefined level. For privacy measure, we consider both the probability of correctly detecting the true value of the secret and the mutual information (MI) between the secret and the released data. We formulate both problems as partially observable Markov decision processes (POMDPs), and numerically solve them by advantage actor-critic (A2C) deep reinforcement learning (DRL). We evaluate the privacy-utility trade-off (PUT) of the proposed policies on both the synthetic data and smoking activity dataset, and show their validity by testing the activity detection accuracy of the SP modeled by a long short-term memory (LSTM) neural network.