SECURETRADE: ACHIEVING TRUTHFULNESS AND PRIVACY IN DATA MARKETPLACES

Abstract

The rapid growth of digital data marketplaces has raised significant concerns regarding data authenticity, user privacy, and fair trade practices. SECURETRADE is proposed as a framework that ensures both truthfulness and privacy preservation in data transactions, thereby enhancing trust and efficiency in data-driven economies. The study examines mechanisms such as cryptographic protocols, secure multiparty computation, and incentive-compatible auction models to address issues of misreporting, data leakage, and trust deficits between buyers and sellers. By integrating privacypreserving algorithms with truthful data valuation techniques, SECURETRADE establishes a secure ecosystem where participants are motivated to share reliable data without compromising confidentiality. The framework further emphasizes regulatory compliance and ethical considerations, aligning technological innovation with responsible data governance. This approach highlights the potential of SECURETRADE to not only safeguard sensitive information but also to foster sustainable and transparent growth in global data marketplaces.