(19)
(11)EP 3 525 388 A3

(12)EUROPEAN PATENT APPLICATION

(88)Date of publication A3:
27.11.2019 Bulletin 2019/48

(43)Date of publication A2:
14.08.2019 Bulletin 2019/33

(21)Application number: 19153349.6

(22)Date of filing:  23.01.2019
(51)Int. Cl.: 
H04L 9/00  (2006.01)
G06N 20/00  (2019.01)
(84)Designated Contracting States:
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
Designated Extension States:
BA ME
Designated Validation States:
KH MA MD TN

(30)Priority: 08.02.2018 US 201815892246

(71)Applicant: Apple Inc.
Cupertino, CA 95014 (US)

(72)Inventors:
  • BHOWMICK, Abhishek
    Cupertino, CA 95014 (US)
  • VYRROS, Andrew H.
    Cupertion, CA 95014 (US)
  • ROGERS, Ryan M.
    Cupertino, CA 95014 (US)

(74)Representative: Barnfather, Karl Jon 
Withers & Rogers LLP 4 More London Riverside
London SE1 2AU
London SE1 2AU (GB)

  


(54)PRIVATIZED MACHINE LEARNING USING GENERATIVE ADVERSARIAL NETWORKS


(57) One embodiment provides for a mobile electronic device comprising a non-transitory machine-readable medium to store instructions, the instructions to cause the mobile electronic device to receive a set of labeled data from a server; receive a unit of data from the server, the unit of data of a same type of data as the set of labeled data; determine a proposed label for the unit of data via a machine learning model on the mobile electronic device, the machine learning model to determine the proposed label for the unit of data based on the set of labeled data from the server and a set of unlabeled data associated with the mobile electronic device; encode the proposed label via a privacy algorithm to generate a privatized encoding of the proposed label; and transmit the privatized encoding of the proposed label to the server.