EMOJI FREQUENCY DETECTION AND DEEP LINK FREQUENCY
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Abstract
Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
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Citations
13 Claims
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1. (canceled)
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2. A computer-implemented method practiced on a set of one or more servers each comprising at least one hardware processor, the method comprising:
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receiving a batch of differentially private term sketches comprising differentially private sketches of terms known to the set of one or more servers, the differentially private sketches received from a plurality of client devices; generating, or retrieving, a differentially private sketch of each of a set of known terms on the set of one or more servers, thereby generating a set differentially private sketches of known terms; selecting a differentially private sketch from the batch; querying, by the set of one or more servers, the set of differentially private sketches of known terms to find a match to the selected differentially sketch, the matching selected differentially private sketch, W, having dimensions of k rows and m columns, wherein each rowε
[k] corresponds to a hash function in a set of k hash functions, H; andadding, by the set of one or more servers, the selected differentially private sketch data to the matching differentially private sketch. - View Dependent Claims (3, 4, 5, 6, 7)
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8. A non-transitory machine readable media storing executable instructions which when executed by a set of one or more servers cause a method practiced on the set of one or more servers each comprising at least one hardware processor, the method comprising:
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receiving a batch of differentially private term sketches comprising differentially private sketches of terms known to the set of one or more servers, the differentially private sketches received from a plurality of client devices; generating, or retrieving, a differentially private sketch of each of a set of known terms on the set of one or more servers, thereby generating a set differentially private sketches of known terms; selecting a differentially private sketch from the batch; querying, by the set of one or more servers, the set of differentially private sketches of known terms to find a match to the selected differentially sketch, the matching selected differentially private sketch, W, having dimensions of k rows and m columns, wherein each rowε
[k] corresponds to a hash function in a set of k hash functions, H; andadding, by the set of one or more servers, the selected differentially private sketch data to the matching differentially private sketch. - View Dependent Claims (9, 10, 11, 12, 13)
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Specification