Differentially Private User Data Perturbation with Multi-Level Privacy Controls
Abstract
Service providers typically collect user data for profiling users in order to provide high-quality services, yet this brings up user privacy concerns. One hand, service providers oftentimes need to analyze multiple user data attributes that usually have different privacy concern levels. On the other hand, users often pose different trusts towards different service providers based on their reputation. However, it is unrealistic to repeatedly ask users to specify privacy levels for each data attribute towards each service provider. To solve this problem, we develop the first lightweight and provably framework that not only guarantees differential privacy on both service provider and different data attributes but also allows configurable utility functions based on service needs. Using various large-scale real-world datasets, our solution helps to significantly improve the utility up to 5 times with negligible computational overhead, especially towards numerous low reputed service providers in practice.
Cite
Text
Shen et al. "Differentially Private User Data Perturbation with Multi-Level Privacy Controls." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46227-1_8Markdown
[Shen et al. "Differentially Private User Data Perturbation with Multi-Level Privacy Controls." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/shen2016ecmlpkdd-differentially/) doi:10.1007/978-3-319-46227-1_8BibTeX
@inproceedings{shen2016ecmlpkdd-differentially,
title = {{Differentially Private User Data Perturbation with Multi-Level Privacy Controls}},
author = {Shen, Yilin and Chen, Rui and Jin, Hongxia},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {112-128},
doi = {10.1007/978-3-319-46227-1_8},
url = {https://mlanthology.org/ecmlpkdd/2016/shen2016ecmlpkdd-differentially/}
}