Privacy-aware Data Trading
release_jistz5vujfdydc2jtr2j5zyuhu
by
Shengling Wang, Lina Shi, Junshan Zhang, Xiuzhen Cheng, Jiguo Yu
2020
Abstract
The growing threat of personal data breach in data trading pinpoints an
urgent need to develop countermeasures for preserving individual privacy. The
state-of-the-art work either endows the data collector with the responsibility
of data privacy or reports only a privacy-preserving version of the data. The
basic assumption of the former approach that the data collector is trustworthy
does not always hold true in reality, whereas the latter approach reduces the
value of data. In this paper, we investigate the privacy leakage issue from the
root source. Specifically, we take a fresh look to reverse the inferior
position of the data provider by making her dominate the game with the
collector to solve the dilemma in data trading. To that aim, we propose the
noisy-sequentially zero-determinant (NSZD) strategies by tailoring the
classical zero-determinant strategies, originally designed for the
simultaneous-move game, to adapt to the noisy sequential game. NSZD strategies
can empower the data provider to unilaterally set the expected payoff of the
data collector or enforce a positive relationship between her and the data
collector's expected payoffs. Both strategies can stimulate a rational data
collector to behave honestly, boosting a healthy data trading market. Numerical
simulations are used to examine the impacts of key parameters and the feasible
region where the data provider can be an NSZD player. Finally, we prove that
the data collector cannot employ NSZD to further dominate the data market for
deteriorating privacy leakage.
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