# Achieving Data Truthfulness and Privacy Preservation in Data Markets

## TL;DR

This paper introduces TPDM, a system that ensures data truthfulness and privacy preservation in data markets through cryptographic techniques, enabling efficient verification and confidentiality in large-scale data trading.

## Contribution

The paper presents TPDM, a novel framework combining data truthfulness verification with privacy preservation using homomorphic encryption and identity-based signatures.

## Key findings

- TPDM effectively verifies data truthfulness and privacy in large-scale markets.
- It maintains low computational and communication overheads.
- Performance evaluated on real-world datasets shows promising results.

## 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.