# Managing your Private and Public Data: Bringing down Inference Attacks against your Privacy

arXiv: https://arxiv.org/abs/1408.3698 · HTML: https://mlsanity.com/paper/1408.3698/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2014.

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trust](https://mlsanity.com/paper/2405.07061/) (2024)
- [Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility](https://mlsanity.com/paper/2207.04895/) (2022)
- [Model Inversion Attacks: A Survey of Approaches and Countermeasures](https://mlsanity.com/paper/2411.10023/) (2024)
- [VFLAIR-LLM: A Comprehensive Framework and Benchmark for Split Learning of LLMs](https://mlsanity.com/paper/2508.03097/) (2025)
- [Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data](https://mlsanity.com/paper/2407.18564/) (2024)
- [Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks](https://mlsanity.com/paper/2205.14440/) (2022)
- [Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition](https://mlsanity.com/paper/2404.02696/) (2024)
- [HyObscure: Hybrid Obscuring for Privacy-Preserving Data Publishing](https://mlsanity.com/paper/2112.07850/) (2021)
- [Inference Privacy: Properties and Mechanisms](https://mlsanity.com/paper/2411.18746/) (2024)

## Abstract (arXiv)

We propose a practical methodology to protect a user's private data, when he wishes to publicly release data that is correlated with his private data, in the hope of getting some utility. Our approach relies on a general statistical inference framework that captures the privacy threat under inference attacks, given utility constraints. Under this framework, data is distorted before it is released, according to a privacy-preserving probabilistic mapping. This mapping is obtained by solving a convex optimization problem, which minimizes information leakage under a distortion constraint. We address practical challenges encountered when applying this theoretical framework to real world data. On one hand, the design of optimal privacy-preserving mechanisms requires knowledge of the prior distribution linking private data and data to be released, which is often unavailable in practice. On the other hand, the optimization may become untractable and face scalability issues when data assumes values in large size alphabets, or is high dimensional. Our work makes three major contributions. First, we provide bounds on the impact on the privacy-utility tradeoff of a mismatched prior. Second, we show how to reduce the optimization size by introducing a quantization step, and how to generate privacy mappings under quantization. Third, we evaluate our method on three datasets, including a new dataset that we collected, showing correlations between political convictions and TV viewing habits. We demonstrate that good privacy properties can be achieved with limited distortion so as not to undermine the original purpose of the publicly released data, e.g. recommendations.