Achieving Data Truthfulness and Privacy Preservation in Data Markets

Abstract

As a significant business paradigm, many online information platforms have emerged to satisfy society's 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 paper, we propose TPDM, which efficiently integrates data 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 distribution fitting 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.

Research on Adaptive Relationship Between Trust and Privacy in Cloud Service

“…In [17], users can decide to publish data items according to the aggregation opinions, who can adjust the parameters to make a balance between data sharing and privacy protection, but the privacy threshold setting was random. Niu and Zheng et al in [18] proposed an effective combination of authenticity and privacy protection, which adopted encryption and signature to effectively maintain identity protection and data confidentiality; however, it was difficult to adapt to the real data service market. Pham and Yeo et al in [19] designed a context-aware trust management scheme and proposed a secure and flexible framework to manage trust and privacy; however, the protocol was complex and the practicability was low.…”

Pricing Personal Data Based on Data Provenance

“…In [30], Acquisti et al indicated that privacy valuations are highly dependent on subtle scenarios and are inconsistent. Niu et al [31] proposed an effective data market security model, which can guarantee data authenticity and protect privacy. Nget et al [32] considered a practical personal data trading framework that strikes a balance between money and privacy.…”

TEEDA: An Interactive Platform for Matching Data Providers and Users in the Data Marketplace

“…Previous papers have reported research on data marketplaces and data exchange platforms where data are traded as exchangeable economic goods [18,19]. These studies have explored game theory [20], privacy models [21,22], a market model for innovative collaboration [5], a secure model using blockchain [10], pricing mechanisms of data [23,24], and the complex network approach to conceptualize data exchange platforms [25]. Therefore, considering data matching in the data marketplace is a natural extension of these previous works.…”