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When collecting information, local differential privacy (LDP) alleviatesprivacy concerns of users because their private information is randomizedbefore being sent it to the central aggregator. LDP imposes large amount ofnoise as each user executes the randomization independently. To address thisissue, recent work introduced an intermediate server with the assumption thatthis intermediate server does not collude with the aggregator. Under thisassumption, less noise can be added to achieve the same privacy guarantee asLDP, thus improving utility for the data collection task.  This paper investigates this multiple-party setting of LDP. We analyze thesystem model and identify potential adversaries. We then make two improvements:a new algorithm that achieves a better privacy-utility tradeoff; and a novelprotocol that provides better protection against various attacks. Finally, weperform experiments to compare different methods and demonstrate the benefitsof using our proposed method.

Improving Utility and Security of the Shuffler-based Differential Privacy