Abstract

Abstract: Many online information platforms have emerged as an essential business model in responding to society's need for personal data where a service provider obtains raw data from data suppliers and then supplies data customers with value added services. However, data consumers have a big difficulty with the data trading layer, namely how to verify that data is actually obtained and processed by the service provider. In addition, data contributors typically hesitate to give data consumers sensitive personal data or their genuine names. Truthfulness as an approach for identifying contributors is provided. If a user buys a product, he or she can submit an evaluation to the system to see if the contributors are allowed. While data contributors must lawfully provide their own data, they cannot personalize others under Data Verification and Privacy Preservation. In addition, the service provider is obliged to correctly collect and handle data. In addition, the personal identification information of data contributors and the sensitive raw data are properly protected. We also employed two alternative data services for data verification and privacy preservation and examined their performance on two real-world datasets carefully.Keywords: Access control (AC); Fair and Privacy-Preserving Deep Learning (FPPDL); Emergency demand response (EDR);Advanced Encryption Standard(AES)