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A geo-marketplace allows users to be paid for their location data. Users concerned about privacy may want to charge more for data that pinpoints their location accurately, but may charge less for data that is more vague. A buyer would prefer to minimize data costs, but may have to spend more to get the necessary level of accuracy. We call this interplay between privacy, utility, and price spatial privacy pricing. We formalize the issues mathematically with an example problem of a buyer deciding whether or not to open a restaurant by purchasing location data to determine if the potential number of customers is sufficient to open. The problem is expressed as a sequential decision making problem, where the buyer first makes a series of decisions about which data to buy and concludes with a decision about opening the restaurant or not. We present two algorithms to solve this problem, including experiments that show they perform better than baselines.
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Preserving Buyer-Privacy in Decentralized Supply Chain Marketplaces
Technology is being used increasingly for lowering the trust barrier in domains where collaboration and cooperation are necessary, but reliability and efficiency are critical due to high stakes. An example is an industrial marketplace where many suppliers must participate in production while ensuring reliable outcomes; hence, partnerships must be pursued with care. Online marketplaces like Xometry facilitate partnership formation by vetting suppliers and mediating the marketplace. How- ever, such an approach requires that all trust be vested in the middleman. This centralizes control, making the system vulnerable to being biased toward specific providers. The use of blockchains is now being explored to bridge the trust gap needed to support decentralizing marketplaces, allowing suppliers and customers to interact more directly by using the information on the blockchain. A typical scenario is the need to preserve privacy in certain interactions initiated by the buyer (e.g., protecting a buyer’s intellectual property during outsourcing negotiations). In this work, we initiate the formal study of matching between suppliers and buyers when buyer-privacy is required for some marketplace interactions and make the following contributions. First, we devise a formal security definition for private interactive matching in the Universally Composable (UC) Model that captures the privacy and correctness properties expected in specific supply chain marketplace interactions. Second, we provide a lean protocol based on any programmable blockchain, anonymous group signatures, and public-key encryption. Finally, we implement the protocol by instantiating some of the blockchain logic by extending the BigChainDB blockchain platform.
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- Award ID(s):
- 1764025
- PAR ID:
- 10378263
- Editor(s):
- Nicola Dragoni, Joaquin Garcia-Alfaro
- Date Published:
- Journal Name:
- CBT: International Workshop on Cryptocurrencies and Blockchain Technology
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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