期刊
IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2019)
卷 -, 期 -, 页码 568-576出版社
IEEE
DOI: 10.1109/infocom.2019.8737579
关键词
Data Trading; Data Privacy; Time-Series Data
类别
资金
- National Key R&D Program of China [2018YFB1004703]
- China NSF [61672348, 61672353, 61472252, 61872238]
- Huawei Innovation Research Program [HO2018085286]
- State Key Laboratory of Air Traffic Management System and Technology [SKLATM20180X]
With the commoditization of personal data, pricing privacy has become an intriguing topic. In this paper, we study time-series data trading from the perspective of a data broker in data markets. We thus propose HORAE, which is a PulTerfisll privacy based framewOrk for tRAding timE-series data. HORAE first employs PufTerfish privacy to quantify privacy losses under temporal correlations, and compensates data owners with distinct privacy strategies in a satisfying way. Besides, HORAE not only guarantees good profitability at the data broker, but also ensures arbitrage freeness against cunning data consumers. We further apply HORAE to physical activity monitoring, and extensively evaluate its performance on the real-world Activity Recognition with Ambient Sensing (ARAS) dataset. Our analysis and evaluation results reveal that HORAE compensates data owners in a more fine-grained manner than entry/group differential privacy based approaches, well controls the profit ratio of the data broker, and thwarts arbitrage attacks launched by data consumers.
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