Privacy-preserved data trading via verifiable data disturbance

• Main Authors: ZHANG, Man, LI, Xinghua, REN, Yanbing, LUO, Bin, MIAO, Yinbin, LIU, Ximeng, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Privacy; Differential Privacy; Servers; Games; Pricing; Public Key; Nash Equilibrium; Availability Requirement; Data Trading; Data Storage Systems; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/9859
• Institution: Singapore Management University

To motivate data owner (DO) to trade data, the existing data trading allows DO to sell the disturbed data to the data consumer (DC), where the disturbance parameter and the data price are negotiated by them, and DO independently adds the disturbance noise to data (usually continuous type) following the negotiation result. However, DOs may violate the negotiated parameter and add more noise to data while obtaining the negotiated price, which damages DC's disturbed data availability. This deficiency is rooted in the absence of supervision and verifiability on DOs’ independent disturbances. Aiming at the above problem, we devise a privacy-preserved data trading via verifiable data disturbance. Specifically, the honest-but-curious disturbance server (DS) is introduced to generate encrypted verifiable disturbance noises, and secretly distribute noises to DOs referring to the method of private information retrieval. Using homomorphic encryption, DOs finish data disturbance without knowing noises’ specific sizes. Subsequently, DC selects DOs to verify with our proposed anti-forgery verification, where the anti-forgery on both disturbance noise and original data guarantees verification correctness. Theoretical analysis proves that DOs’ original data is preserved in data trading. Extensive experiments using the real-world dataset demonstrate that our scheme can detect more than 80% of malicious DOs and decrease their utilities to punish malicious disturbance compared with existing works.