Vizard: A Metadata-hiding Data Analytic System with End-to-End Policy Controls

Chengjun Cai; Yichen Zang; Cong Wang; Xiaohua Jia; Qian Wang

Paper 2022/1115

Vizard: A Metadata-hiding Data Analytic System with End-to-End Policy Controls

Chengjun Cai, City University of Hong Kong Dongguan Research Institute

Yichen Zang, City University of Hong Kong

Cong Wang, City University of Hong Kong

Xiaohua Jia, City University of Hong Kong

Qian Wang, Wuhan University

Abstract

Owner-centric control is a widely adopted method for easing owners' concerns over data abuses and motivating them to share their data out to gain collective knowledge. However, while many control enforcement techniques have been proposed, privacy threats due to the metadata leakage therein are largely neglected in existing works. Unfortunately, a sophisticated attacker can infer very sensitive information based on either owners' data control policies or their analytic task participation histories (e.g., participating in a mental illness or cancer study can reveal their health conditions). To address this problem, we introduce , a metadata-hiding analytic system that enables privacy-hardened and enforceable control for owners. is built with a tailored suite of lightweight cryptographic tools and designs that help us efficiently handle analytic queries over encrypted data streams coming in real-time (like heart rates). We propose extension designs to further enable advanced owner-centric controls (with AND, OR, NOT operators) and provide owners with release control to additionally regulate how the result should be protected before deliveries. We develop a prototype of that is interfaced with Apache Kafka, and the evaluation results demonstrate the practicality of for large-scale and metadata-hiding analytics over data streams.

Note: This is the full version of our paper accepted by ACM CCS 2022.

Metadata

Available format(s): PDF
Category: Applications
Publication info: Published elsewhere. ACM CCS 2022
DOI: 10.1145/3548606.3559349
Keywords: Data analytics; Metadata privacy
Contact author(s): chengjun cai @ cityu edu cn
yichen zang @ my cityu edu hk
congwang @ cityu edu hk
csjia @ cityu edu hk
qianwang @ whu edu cn
History: 2022-09-15: last of 3 revisions; 2022-08-29: received; See all versions
Short URL: https://ia.cr/2022/1115
License: CC BY

BibTeX

@misc{cryptoeprint:2022/1115,
      author = {Chengjun Cai and Yichen Zang and Cong Wang and Xiaohua Jia and Qian Wang},
      title = {Vizard: A Metadata-hiding Data Analytic System with End-to-End Policy Controls},
      howpublished = {Cryptology {ePrint} Archive, Paper 2022/1115},
      year = {2022},
      doi = {10.1145/3548606.3559349},
      url = {https://eprint.iacr.org/2022/1115}
}