# Don't let Google know I'm lonely!

arXiv: https://arxiv.org/abs/1504.08043 · HTML: https://mlsanity.com/paper/1504.08043/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2015.

## Built on by (most cited first, 2 of 2)

- [Stronger Privacy for Federated Collaborative Filtering with Implicit Feedback](https://mlsanity.com/paper/2105.03941/) (2021)
- [Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces](https://mlsanity.com/paper/2311.01722/) (2023)

## Abstract (arXiv)

From buying books to finding the perfect partner, we share our most intimate wants and needs with our favourite online systems. But how far should we accept promises of privacy in the face of personal profiling? In particular we ask how can we improve detection of sensitive topic profiling by online systems? We propose a definition of privacy disclosure we call ε-indistinguishability from which we construct scalable, practical tools to assess an adversaries learning potential. We demonstrate our results using openly available resources, detecting a learning rate in excess of 98% for a range of sensitive topics during our experiments.