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 the disclosure of sensitive
information. Despite considerable research that has focused on different
aspects of privacy-enhancing data markets for the IoT, none of the solutions
proposed so far seems to find considerable 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. 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 remaining challenges 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.

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