Protecting Data Buyer Privacy in Data Markets

TL;DR

This paper explores the challenge of safeguarding data buyer privacy in data markets, proposing models and approaches to balance privacy concerns with purchase costs, supported by experimental validation.

Contribution

It introduces a novel framework for protecting data buyer privacy and analyzes the trade-offs between privacy and cost in data market transactions.

Findings

Proposed effective privacy protection models.

Demonstrated trade-offs between privacy levels and costs.

Validated approaches through comprehensive experiments.

Abstract

Data markets serve as crucial platforms facilitating data discovery, exchange, sharing, and integration among data users and providers. However, the paramount concern of privacy has predominantly centered on protecting privacy of data owners and third parties, neglecting the challenges associated with protecting the privacy of data buyers. In this article, we address this gap by modeling the intricacies of data buyer privacy protection and investigating the delicate balance between privacy and purchase cost. Through comprehensive experimentation, our results yield valuable insights, shedding light on the efficacy and efficiency of our proposed approaches.