Revealing the landscape of privacy-enhancing technologies in the context of data markets for the IoT: A systematic literature review. (November 2022)

• Record Type: Journal Article
• Authors: Garrido, Gonzalo Munilla; Sedlmeir, Johannes; Uludağ, Ömer; Alaoui, Ilias Soto; Luckow, Andre; Matthes, Florian
• Is Part Of: Journal of network and computer applications. Volume 207(2022)
• Publication Date: 2022-11
• Subjects: Anonymization -- Big data -- Copy problem -- Data exchange -- Marketplace -- Platform -- Secure computation; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2022.103465
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: IoT data markets in public and private institutions have become increasingly relevant in recent years because of their potential to improve data availability and unlock new business models. However, exchanging data in markets bears considerable challenges related to disclosing sensitive information. Despite considerable research focused on different aspects of privacy-enhancing data markets for the IoT, none of the solutions proposed so far seems to find a practical adoption. Thus, this study aims to organize the state-of-the-art solutions, analyze and scope the technologies that have been suggested in this context, and structure the remaining challenges to determine areas where future research is required. To accomplish this goal, we conducted a systematic literature review on privacy enhancement in data markets for the IoT, covering 50 publications dated up to July 2020, and provided updates with 24 publications dated up to May 2022. Our results indicate that most research in this area has emerged only recently, and no IoT data market architecture has established itself as canonical. Existing solutions frequently lack the required combination of anonymization and secure computation technologies. Furthermore, there is no consensus on the appropriate use of blockchain technology for IoT data markets and a low degree of leveraging existing libraries or reusing generic data market architectures. We also identified significant challenges remaining, such as the copy problem and the recursive enforcement problem that – while solutions have been suggested to some extent – are often not sufficiently addressed in proposed designs. We conclude that privacy-enhancing technologies need further improvements to positively impact data markets so that, ultimately, the value of data is preserved through data scarcity and users' privacy and businesses-critical information are protected. Highlights: The practicality of privacy technologies must advance for data markets to flourish. The optimal mix of privacy technologies in data markets is in the development phase. Data market architectures rarely leverage open-source privacy-enhancing libraries. Data market architectures rarely combine anonymization with secure computation. Researchers often employ a blockchain despite a lack of clarity on its utility.