Patent application EP17196277A (EP), filed 2017-10-13
Publication number: EP3471005A1
Title: ARTIFICIAL NEURAL NETWORK
Abstract: According to an example aspect of the present invention, there is provided an apparatus comprising a memory configured to store training data, at least one processor configured to provide a trusted execution environment, wherein the apparatus is configured to run, in the trusted execution environment, a training process configured to obtain parameters of a neural network, using the training data.
IPC codes: G06N   3/    00            A I
G06F  21/    53            A I
G06F  21/    57            A I
G06F  21/    62            A I
H04L  29/    06            A I
H04W  12/    02            A I
G06N  20/    00            A I
G06N   3/    02            A I
G06N   3/    04            A I
G06N   3/    08            A I
Claims: number: 1
text: An apparatus comprising:
number: 2
text: The apparatus according to claim 1, wherein apparatus further comprises means for providing another execution environment, the trusted execution environment being provided with at least one hardware and/or software security feature not provided for the another execution environment.
number: 3
text: The apparatus according to claim 1 or 2, wherein the means for storing are accessible to at least two execution environments provided by the at least one processor.
number: 4
text: The apparatus according to any of claims 1 - 3, wherein the means for providing the trusted execution environment are configured to decrypt the training data before using it in the training process.
number: 5
text: The apparatus according to any of claims 1 - 4, wherein the means for providing the trusted execution environment are further configured to obtain a measurement of a computer program of the training process, and to cause the measurement to be provided to a party outside of the apparatus.
number: 6
text: The apparatus according to claim 5, wherein the measurement comprises obtaining a hash of the computer program of the training process.
number: 7
text: The apparatus according to any of claims 1- 6, wherein the means for providing the trusted execution environment are configured to cause the parameters of the neural network to be exported from the apparatus.
number: 8
text: The apparatus according to claim 7, wherein the means for providing the trusted execution environment are configured to cause randomly generated noise to be added to the parameters before exporting them from the apparatus.
number: 9
text: The apparatus according to any of claims 1- 8, wherein the means for providing the trusted execution environment are configured to run the neural network, once the parameters have been obtained, in the trusted execution environment.
number: 10
text: The apparatus according to claim 9, wherein the apparatus is configured to cause randomly generated noise to be added to a result obtained from the neural network, before causing the response to be transmitted from the apparatus.
number: 11
text: The apparatus according to claim 9 or 10, wherein apparatus is configured to run the neural network in the trusted execution environment at most a preconfigured number of times.
number: 12
text: A method, comprising:
number: 13
text: An apparatus comprising:
number: 14
text: A method comprising:
number: 15
text: A computer program configured to cause a method in accordance with either claim 12 or claim 14 to be performed, when run.
Office action: search_report, dated 2019-04-17
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: - NICOLAS PAPERNOT ET AL.: "Semi-Supervised Knowledge Transfer for Deep Learning from Private Training Data", INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS 2017, 3 March 2017 (2017-03-03)
ref_type: npl
severity: background
ref_id: US2002173295A1
ref_type: patent
severity: background
ref_id: - MIKHAIL I GOFMAN ET AL: "Multimodal biometrics for enhanced mobile device security", COMMUNICATIONS OF THE ACM, ASSOCIATION FOR COMPUTING MACHINERY, INC, UNITED STATES, vol. 59, no. 4, 23 March 2016 (2016-03-23), pages 58 - 65, XP058078679, ISSN: 0001-0782, DOI: 10.1145/2818990
ref_type: npl
severity: background
ref_id: - DUCRAY BENOIT ET AL: "Gesture recognition implemented on a personal limited device", 2017 8TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS), IEEE, 4 April 2017 (2017-04-04), pages 171 - 176, XP033094358, DOI: 10.1109/IACS.2017.7921966
ref_type: npl
severity: background
ref_id: - REZA SHOKRI ET AL: "Privacy-Preserving Deep Learning", PROCEEDINGS OF THE 22ND ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, CCS '15, 12 October 2015 (2015-10-12), New York, New York, USA, pages 1310 - 1321, XP055394102, ISBN: 978-1-4503-3832-5, DOI: 10.1145/2810103.2813687
ref_type: npl
severity: background
ref_id: US2002173295
ref_type: patent
severity: background
ref_id: - MIKHAIL I GOFMAN ET AL, "Multimodal biometrics for enhanced mobile device security", COMMUNICATIONS OF THE ACM, ASSOCIATION FOR COMPUTING MACHINERY, INC, UNITED STATES, (20160323), vol. 59, no. 4, doi:10.1145/2818990, ISSN 0001-0782, pages 58 - 65, XP058078679 [X] 1-15 * figures 1-3 * * pages  60-62 * * pages  63-65 *
ref_type: npl
severity: novelty_destroying
ref_id: - DUCRAY BENOIT ET AL, "Gesture recognition implemented on a personal limited device", 2017 8TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS), IEEE, (20170404), doi:10.1109/IACS.2017.7921966, pages 171 - 176, XP033094358 [X] 1-15 * pages  171-172 * * page  175 *
ref_type: npl
severity: novelty_destroying
ref_id: - REZA SHOKRI ET AL, "Privacy-Preserving Deep Learning", PROCEEDINGS OF THE 22ND ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, CCS '15, New York, New York, USA, (20151012), doi:10.1145/2810103.2813687, ISBN 978-1-4503-3832-5, pages 1310 - 1321, XP055394102 [A] 1-15 * pages  1-3 * * pages  4-5 *
ref_type: npl
severity: background
Final disposition: pending (2019-04-17)