Analytics interface with privacy management

    公开(公告)号:US11366923B2

    公开(公告)日:2022-06-21

    申请号:US16724281

    申请日:2019-12-21

    Abstract: In an embodiment, the disclosed technologies include receiving a query that requests aggregate information about entity event data relating to digital content delivered digitally by an entity management system to entities of the entity management system, the query associated with a requester account; determining a first privacy allocation for the requester account; determining a first privacy value, the first privacy value computed based on the query and a selected privacy algorithm; deducting the first privacy value from the first privacy allocation to produce a first privacy balance; causing executing of the query on the entity event data and providing a result set in response to the query only if the first privacy balance indicates that the first privacy allocation has not been depleted.

    ANALYTICS INTERFACE WITH PRIVACY MANAGEMENT

    公开(公告)号:US20210192068A1

    公开(公告)日:2021-06-24

    申请号:US16724281

    申请日:2019-12-21

    Abstract: In an embodiment, the disclosed technologies include receiving a query that requests aggregate information about entity event data relating to digital content delivered digitally by an entity management system to entities of the entity management system, the query associated with a requester account; determining a first privacy allocation for the requester account; determining a first privacy value, the first privacy value computed based on the query and a selected privacy algorithm; deducting the first privacy value from the first privacy allocation to produce a first privacy balance; causing executing of the query on the entity event data and providing a result set in response to the query only if the first privacy balance indicates that the first privacy allocation has not been depleted.

    Differentially private top-k selection

    公开(公告)号:US11170131B1

    公开(公告)日:2021-11-09

    申请号:US16527987

    申请日:2019-07-31

    Abstract: Techniques for ensuring differential privacy in top-K selection are provided. In one technique, multiple items and multiple counts are identified in response to a query. For each count, which corresponds to a different item, a noise value is generated and added to the count to generate a noisy value, and the noisy value is added to a set of noisy values that is initially empty. A particular noise value is generated for a particular count and added to the particular count to generate a noisy threshold. The particular noise value is generated using a different technique than the technique used to generate each noise value in the set. Based on the noisy threshold, a subset of the noisy values is identified, where each noisy value in the subset is less than the noisy threshold. A response to the query is generated that excludes items that correspond to the subset.

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