Making Big Money from Small Sensors: Trading Time-Series Data under Pufferfish Privacy

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.