Privacy-preserved data disturbance and truthfulness verification for data trading

• Main Authors: ZHANG, Man, LI, Xinghua, MIAO, Yinbin, LUO, Bin, XU, Wanyun, REN, Yanbing, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Blockchains; Costs; data privacy; Data privacy; data trading; disturbance truthfulness; Noise; origin truthfulness; Privacy; purchase privacy; Servers; Watermarking; Information Security; Portfolio and Security Analysis
• Online Access: https://ink.library.smu.edu.sg/sis_research/8818
• Institution: Singapore Management University

The advanced data trading allows data generator’s (DG) disturbed data to be traded as both initial and reselling trading modes, which meets DG’s raw data privacy and data consumers’ (DCs) vast data requirement. However, the traded data truthfulness verifiability cannot be guaranteed in the privacy-p...