ACHIEVING DATA TRUTHFULNESS AND PRIVACY PRESERVATION IN DATA MARKETS

Abstract:

As a significant business paradigm, many online information platforms have emerged to satisfy societys needs for person-specific data, where a service provider collects raw data from data contributors, and then offers value-added data services to data consumers. However, in the data trading layer, the data consumers face a pressing problem, i.e., how to verify whether the service provider has truthfully collected and processed data? Furthermore, the data contributors are usually unwilling to reveal their sensitive personal data and real identities to the data consumers. In this project, we propose TPDM, which efficiently integrates Truthfulness and Privacy preservation in Data Markets. TPDM is structured internally in an Encrypt-then-Sign fashion, using partially homomorphic encryption and identity-based signature. It simultaneously facilitates batch verification, data processing, and outcome verification, while maintaining identity preservation and data confidentiality. We also instantiate TPDM with a profile matching service and a data distribution service, and extensively evaluate their performances on Yahoo! Music ratings dataset and 2009 RECS dataset, respectively. Our analysis and evaluation results reveal that TPDM achieves several desirable properties, while incurring low computation and communication overheads when supporting large-scale data markets.

Frontend:

DOTNET

Backend:

SQL SERVER

Area:

IEEE PROJECT

Domain:

DATA MINING

Sample Screen:       

Sample Document:  

Full Document Price: Rs.299

(Full Document Contains: Content Page, Abstract, Introduction, Software Description, DFD, ER, UML Diagrams, Modules Description, System Requirements, Testing, Conclusion, Future work, Bibliography, References, Source code, All Screenshots)