EMOJI FREQUENCY DETECTION AND DEEP LINK FREQUENCY

• US 20170359364A1
• Filed: 06/30/2017
• Published: 12/14/2017
• Est. Priority Date: 06/12/2016
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Thakurta, Abhradeep Guha, Vyrros, Andrew H., Vaishampayan, Umesh S., Kapoor, Gaurav, Freudinger, Julien, Prakash, Vipul Ved, Legendre, Arnaud, Duplinsky, Steven
• Granted Patent: US 10,154,054 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 21/6254 by anonymising data, e.g. decorrelating personal data from the owner's identification
• CPC Class Codes: G06F 40/134 Hyperlinking
• CPC Class Codes: G06F 40/242 Dictionaries
• CPC Class Codes: G06F 40/274 Converting codes to words; Guess-ahead of partial word inputs
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 63/0421 Anonymous communication, i.e. the party's identifiers are hidden from the other party or parties, e.g. using an anonymizer
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection

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.

Claims

1. A computer-implemented method practiced on a server comprising at least one hardware processor, the method comprising:

receiving a batch of rows of differentially private term sketches comprising differentially private sketches of terms known to the server, 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 server, thereby generating a set differentially private sketches of known terms;

selecting a row of a differentially private sketch from the batch;

querying the set of differentially private sketches of known terms to find a match to the selected row of a differentially private sketch; and

adding the selected row of the differentially private sketch data to the matching differentially private sketch.

2. The method of claim 2, further comprising:

determining the frequency of the term corresponding to the selected sketch.

3. The method of claim 2, further comprising:

updating an asset catalog having frequencies of known terms with the determined frequencies of known terms generated from the received batch of rows of differentially private sketches, thereby generating an updated asset catalog; and

transmitting the updated asset catalog to at least one client device.

4. The method of claim 2, wherein determining the frequency of a known term comprises:

generating a vector, V, comprising k values, each value obtained from a different one of k rows in the matching differentially private sketch for the term;

determining a sketch count value, C, wherein C is one of;

a median of all values in vector V, or a minimum of all values in vector V; and

determining the frequency of the term by multiplying C by k.

5. A non-transitory machine readable media storing executable instructions which when executed by a set of one or more data processing systems cause the set to perform a method, the method comprising:

receiving a batch of rows of differentially private term sketches comprising differentially private sketches of terms known to the server, 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 server, thereby generating a set differentially private sketches of known terms;

selecting a row of a differentially private sketch from the batch;

querying the set of differentially private sketches of known terms to find a match to the selected row of a differentially private sketch; and

adding the selected row of the differentially private sketch data to the matching differentially private sketch.

6. The media of claim 5, further comprising:

determining the frequency of the term corresponding to the selected sketch.

7. The media of claim 6, further comprising:

updating an asset catalog having frequencies of known terms with the determined frequencies of known terms generated from the received batch of rows of differentially private sketches, thereby generating an updated asset catalog; and

transmitting the updated asset catalog to at least one client device.

8. The media of claim 6, wherein determining the frequency of a known term comprises:

generating a vector, V, comprising k values, each value obtained from a different one of k rows in the matching differentially private sketch for the term;

determining a sketch count value, C, wherein C is one of;

a median of all values in vector V, or a minimum of all values in vector V; and

determining the frequency of the term by multiplying C by k.