Patent application EP21177118A (EP), filed 2021-06-01
Publication number: EP3923210A1
Title: GENERATION OF SYNTHETIC DATA
Abstract: Synthetic data may be used in place of an original dataset to avoid or mitigate disclosure risks pertaining to information of the original dataset. Synthetic data may be generated by optimizing a variable ordering used by a sequential tree generation method. The loss function used in optimizing may be based on a distinguishability between the source data and generated synthetic data.
IPC codes: G06N  20/    10            A I
G06N   5/    00            A I
Claims: number: 1
text: A computer-implemented method of generating synthetic data comprising:
number: 2
text: The computer-implemented method of claim 1, wherein the loss function is based on a distinguishability score between the source dataset and the generated synthetic dataset.
number: 3
text: The computer-implemented method of claim 2, wherein the distinguishability score is computed as a mean square difference of a predicted probability from a threshold value.
number: 4
text: The computer-implemented method of claim 3, wherein the distinguishability score is computed according to:  d = / 1 N ∑ i p i − 0.5 2
number: 5
text: The computer-implemented method of any one of claims 2 to 4, wherein the loss function is a hinge loss function.
number: 6
text: The computer-implemented method of claim 5, wherein the loss function is further based on one or more of:
number: 7
text: The computer-implemented method of any one of claims 1 to 6, wherein optimizing the hyperparameters comprises determining updated hyperparameters according to an optimization algorithm.
number: 8
text: The computer-implemented method of any one of claims 1 to 7, wherein the sequential synthesis method comprises at least one of:
number: 9
text: The computer-implemented method of any one of claims 1 to 8, wherein the generated synthetic dataset or the generated updated synthetic dataset is one of: a partially synthetic dataset and a fully synthetic dataset.
number: 10
text: The computer-implemented method of any one of claims 1 to 9, wherein the hyperparameters comprise a variable order used by the sequential synthesis method.
number: 11
text: The computer-implemented method of any one of claims 1 to 10, wherein the hyperparameters comprise:
number: 12
text: The computer-implemented method of any one of claims 1 to 11, wherein the optimization algorithm comprises at least one of:
number: 13
text: The computer-implemented method of any one of claims 1 to 12, further comp rising outputting the synthetic dataset generated from the optimized variable ordering.
number: 14
text: The computer-implemented method of any one of claims 1, further comprising: evaluating an identity disclosure risk of the synthetic dataset generated from the optimized variable ordering.
number: 15
text: A non-transitory computer readable medium storing instructions, which when executed configure a computing system to perform a computer-implemented method according to any one of claims 1 to 14.
number: 16
text: A computing system for generating synthetic data comprising:
Office action: search_report, dated 2021-12-15
Prior art cited: ref_id: US202063012447P
ref_type: patent
severity: background
ref_id: - DRECHSLER JÖRG ET AL: "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets", COMPUTATIONAL STATISTICS AND DATA ANALYSIS, vol. 55, no. 12, 1 December 2011 (2011-12-01), NL, pages 3232 - 3243, XP055854523, ISSN: 0167-9473, DOI: 10.1016/j.csda.2011.06.006
ref_type: npl
severity: background
ref_id: - GONCALVES EDUARDO CORREA ET AL: "A Genetic Algorithm for Optimizing the Label Ordering in Multi-label Classifier Chains", 2013 IEEE 25TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, IEEE, 4 November 2013 (2013-11-04), pages 469 - 476, XP032564761, ISSN: 1082-3409, [retrieved on 20140207], DOI: 10.1109/ICTAI.2013.76
ref_type: npl
severity: background
ref_id: - JESSE READ ET AL: "Classifier Chains: A Review and Perspectives", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 15 April 2020 (2020-04-15), XP081878624, DOI: 10.1613/JAIR.1.12376
ref_type: npl
severity: background
ref_id: - DANDEKAR ASHISH ET AL: "A Comparative Study of Synthetic Dataset Generation Techniques", 9 August 2018, ICIAP: INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND PROCESSING, 17TH INTERNATIONAL CONFERENCE, NAPLES, ITALY, SEPTEMBER 9-13, 2013. PROCEEDINGS; [LECTURE NOTES IN COMPUTER SCIENCE; LECT.NOTES COMPUTER], SPRINGER, BERLIN, HEIDELBERG, PAGE(S) 387 - 3, ISBN: 978-3-642-17318-9, XP047482891
ref_type: npl
severity: background
ref_id: - GILLIAN M RAAB ET AL: "Guidelines for Producing Useful Synthetic Data", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 12 December 2017 (2017-12-12), XP080842633
ref_type: npl
severity: background
ref_id: - DRECHSLER JÖRG ET AL, "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets", COMPUTATIONAL STATISTICS AND DATA ANALYSIS, NL, (20111201), vol. 55, no. 12, doi:10.1016/j.csda.2011.06.006, ISSN 0167-9473, pages 3232 - 3243, XP055854523 [I] 1-16 * the whole document *
ref_type: npl
severity: background
ref_id: - GONCALVES EDUARDO CORREA ET AL, "A Genetic Algorithm for Optimizing the Label Ordering in Multi-label Classifier Chains", 2013 IEEE 25TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, IEEE, doi:10.1109/ICTAI.2013.76, ISSN 1082-3409, (20131104), pages 469 - 476, (20140207), XP032564761 [A] 1-16 * the whole document *
ref_type: npl
severity: background
ref_id: - JESSE READ ET AL, "Classifier Chains: A Review and Perspectives", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20200415), doi:10.1613/JAIR.1.12376, XP081878624 [A] 1-16 * the whole document *
ref_type: npl
severity: background
ref_id: - DANDEKAR ASHISH ET AL, A Comparative Study of Synthetic Dataset Generation Techniques, ICIAP: INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND PROCESSING, 17TH INTERNATIONAL CONFERENCE, NAPLES, ITALY, SEPTEMBER 9-13, 2013. PROCEEDINGS; [LECTURE NOTES IN COMPUTER SCIENCE; LECT.NOTES COMPUTER], SPRINGER, BERLIN, HEIDELBERG, PAGE(S) 387 - 3, (20180809), ISBN 978-3-642-17318-9, XP047482891 [A] 1-16 * the whole document *
ref_type: npl
severity: background
ref_id: - GILLIAN M RAAB ET AL, "Guidelines for Producing Useful Synthetic Data", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20171212), XP080842633 [A] 1-16 * the whole document *
ref_type: npl
severity: background
Final disposition: pending (2021-12-15)