Quantum Privacy-Preserving Data Analytics
Shenggang Ying, Mingsheng Ying, Yuan Feng

TL;DR
This paper introduces a quantum protocol for privacy-preserving data analytics that significantly enhances privacy protection for both data providers and users compared to classical methods, with high detection probability for dishonest behavior.
Contribution
The paper proposes a novel quantum data mining protocol that ensures better privacy preservation and dishonest behavior detection than existing classical algorithms.
Findings
Quantum protocol guarantees privacy if both parties are honest
Dishonest data users are detected with high probability
Data providers cannot access user privacy when attempting disclosure
Abstract
Data analytics (such as association rule mining and decision tree mining) can discover useful statistical knowledge from a big data set. But protecting the privacy of the data provider and the data user in the process of analytics is a serious issue. Usually, the privacy of both parties cannot be fully protected simultaneously by a classical algorithm. In this paper, we present a quantum protocol for data mining that can much better protect privacy than the known classical algorithms: (1) if both the data provider and the data user are honest, the data user can know nothing about the database except the statistical results, and the data provider can get nearly no information about the results mined by the data user; (2) if the data user is dishonest and tries to disclose private information of the other, she/he will be detected with a high probability; (3) if the data provider tries to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Quantum Mechanics and Applications
