Cloud-Based Privacy-Preserving Collaborative Consumption for Sharing Economy

Abstract

Cloud computing has been a dominant paradigm for a variety of information processing platforms, particularly for enabling various popular applications of sharing economy. However, there is a major concern regarding data privacy on these cloud-based platforms. This work presents novel cloud-based privacy-preserving solutions to support collaborative consumption applications for sharing economy. In typical collaborative consumption, information processing platforms need to enable fair cost-sharing among multiple users for utilizing certain shared facilities and communal services. Our cloud-based privacy-preserving protocols, based on homomorphic Paillier cryptosystems, can ensure that the cloud-based operator can only obtain an aggregate schedule of all users in facility sharing, or a service schedule conforming to service provision rule in communal service sharing, but is unable to track the personal schedules or demands of individual users. More importantly, the participating users are still able to settle cost-sharing among themselves in a fair manner for the incurred costs, without knowing each other's private schedules or demands. Our privacy-preserving protocols involve no other third party who may compromise privacy. We also provide an extensive evaluation study and a proof-of-concept system prototype of our protocols.

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“…To preserve the privacy of location-based services, data outsourced to the cloud should be encrypted and only be able to be queried by registered authorized users in methods detailed by the authors of [271] and [272]. For data sharing services, the authors of [273] state that data should not be shared with a third-party in order to maintain privacy.…”

Blockchain-enabled decentralized privacy-preserving group purchasing for retail energy plans

“…Note that there is an intrinsic trade-off between accuracy and privacy in differential privacy. On the other hand, secure multiparty computation [19,23] traditionally employed garbled circuits [26], which have a high computational complexity, and homomorphic cryptosystems [16,30,35], which need a trusted setup for key generation. Recently, information-theoretical secret-sharing [17,18] has been utilized for secure multi-party computation, which provides high efficiency and requires no trusted third-party setup.…”

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“…There is an intrinsic trade-off between accuracy and privacy in differential privacy. On the other hand, secure multi-party computation [12,17] traditionally employed garbled circuits [20], which have a high computational complexity, and homomorphic cryptosystems [9,27], which need a trusted setup for key generation. Recently, information-theoretical secret-sharing [10,11] has been utilized for secure multi-party computation, which provides high efficiency and requires no trusted third-party setup.…”