Differentially private frequency deduplication

    公开(公告)号:US12158868B2

    公开(公告)日:2024-12-03

    申请号:US17911881

    申请日:2021-06-23

    Applicant: Google LLC

    Abstract: Systems and methods are disclosed herein for improved per-frequency counting systems that record interactions between individuals and a group of providers while maintaining differential privacy. A protocol may be defined that specifies frequency bins to categorize identifiers corresponding to individuals. A provider may generate a plurality of private sketches, each corresponding to a plurality of frequencies defined in the protocol. Frequency data is determined for each identifier. Identifiers are encoded into the private sketches corresponding to the identifiers' associated frequency. The plurality of private sketches from each provider in the group of providers are combined to generate a deduplicated distribution across the group. In one implementation, the private sketches of each provider are sequentially merged until all sketches have been combined, from which the total distribution can be estimated.

    Differentially Private Frequency Deduplication

    公开(公告)号:US20230144763A1

    公开(公告)日:2023-05-11

    申请号:US17911881

    申请日:2021-06-23

    Applicant: Google LLC

    CPC classification number: G06F16/215 G06F16/2237 G06F16/2365 G06F21/6254

    Abstract: Systems and methods are disclosed herein for improved per-frequency counting systems that record interactions between individuals and a group of providers while maintaining differential privacy. A protocol may be defined that specifies frequency bins to categorize identifiers corresponding to individuals. A provider may generate a plurality of private sketches, each corresponding to a plurality of frequencies defined in the protocol. Frequency data is determined for each identifier. Identifiers are encoded into the private sketches corresponding to the identifiers’ associated frequency. The plurality of private sketches from each provider in the group of providers are combined to generate a deduplicated distribution across the group. In one implementation, the private sketches of each provider are sequentially merged until all sketches have been combined, from which the total distribution can be estimated.

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