Patent application EP20169851A (EP), filed 2020-04-16
Publication number: EP3726414A1
Title: SYSTEM AND METHODS FOR NATURAL PSEUDONYMIZATION OF TEXT
Abstract: The present disclosure directs to systems and methods for natural pseudonymization of text. A natural pseudonym has at least one information attribute same as a piece of sensitive text information. The systems and methods can identify sensitive text information, select a natural pseudonym, and modify a data stream of text data by replacing the piece of sensitive text information with the natural pseudonym.
IPC codes: G06F  21/    62            A I
G06F  40/   284            A I
Claims: number: 1
text: A method implemented by a computer system having one or more processors and memories, comprising:
number: 2
text: The method of claim 1, wherein the one or more information attributes comprise at least one of a gender, an age, an ethnicity, an information type, a number of letters, a capitalization pattern, a geographic origin, and street address characteristics of a location.
number: 3
text: The method of claim 1 or 2, wherein the natural pseudonym has at least two information attributes same as corresponding information attributes of the piece of sensitive text information.
number: 4
text: The method of any of claims 1 to 3, wherein the natural pseudonym enables a same downstream processing behavior as the sensitive text information.
number: 5
text: The method of any of claims 1 to 4, wherein the sensitive text information includes a first date range, and wherein the natural pseudonym has a second date range having a same duration as the first date range.
number: 6
text: The method of any of claims 1 to 5, wherein at least one classifier from the at least two other classifiers is a term-morphological-analysis classifier, and at least one classifier from the at least two other classifiers is a term-context-based classifier.
number: 7
text: The method of claim 6, wherein term-morphological-analysis classifier is a prefix-suffix-based type classifier, a subword-compound-based type classifier, or combinations thereof.
number: 8
text: The method of claim 6 or 7, wherein the term-context-based classifier is selected from the group consisting of a multi-word-phrase-based type classifier, a token/type-ngram-context based classifier, a glue-patterns-in-context-based classifier, a document-region-based type classifier or a type-specific rule-based type classifier, and combinations thereof.
number: 9
text: The method of any of claims 1 to 8, wherein identifying a piece of sensitive text information comprises
identifying a plurality of pieces of sensitive text information in the received data stream;
wherein selecting a natural pseudonym comprises selecting a plurality of natural pseudonyms by a pseudonymization processor, wherein each natural pseudonym corresponds to one of the plurality of pieces of sensitive text information on a one-by-one basis; and
wherein modifying the data stream comprises replacing the plurality of pieces of sensitive text information with the plurality of natural pseudonyms.
number: 10
text: The method of claim 9, wherein the plurality of natural pseudonyms are selected on the one or more information attributes such that an output of a data processing software processing the modified data stream is the same as an output of the data processing software processing the data stream that is not modified.
number: 11
text: The method of any of claims 1 to 10, wherein receiving the data stream of text data comprises text data from a document, further comprising tokenizing the text data to form a plurality of tokens.
number: 12
text: The method of claim 11, wherein identifying the piece of sensitive text information comprises:
number: 13
text: The method of any of claims 1 to 12, wherein the natural pseudonym preserves byte offset relative to a beginning of the document.
number: 14
text: The method of any of claims 1 to 13, wherein modifying the document by replacing the piece of sensitive text information with the natural pseudonym forms a pseudonymized document; and the method further comprises:
transmitting the pseudonymized document to a downstream processor, wherein the downstream processor comprises a coding application configured to generate a coding output that includes codes and evidence information.
number: 15
text: A system for pseudoanonymization of sensitive text information comprising:
Office action: search_report, dated 2020-10-21
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: US2016030075A1
ref_type: patent
severity: background
ref_id: - DEMPSTER ET AL.: "Maximum Likelihood from Incomplete Data via the EM Algorithm", JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B, vol. 39, no. 1, 1977, pages 1 - 38
ref_type: npl
severity: background
ref_id: - GALEWILLIAM A.KENNETH W. CHURCHDAVID YAROWSKY: "Discrimination decisions for 100,000-dimensional spaces", ANNALS OF OPERATIONS RESEARCH, vol. 55, no. 2, 1995, pages 323 - 344
ref_type: npl
severity: background
ref_id: - YAROWSKYDAVID.: "Decision lists for lexical ambiguity resolution: Application to accent restoration in Spanish and French", ROCEEDINGS OF THE 32ND ANNUAL MEETING ON ASSOCIATION FOR COMPUTATIONAL LINGUISTICS. ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, 1994
ref_type: npl
severity: background
ref_id: - YAROWSKYDAVID.: "Unsupervised word sense disambiguation rivaling supervised methods", 33RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, 1995
ref_type: npl
severity: background
ref_id: - ABNEYSTEVEN: "Understanding the Yarowsky algorithm", COMPUTATIONAL LINGUISTICS, vol. 30, no. 3, 2004, pages 365 - 395, XP058224146, DOI: 10.1162/0891201041850876
ref_type: npl
severity: background
ref_id: - B. PESKINJ. NAVRATILJ. ABRAMSOND. JONESD. KLUSACEKD.A. REYNOLDCHENGCHENG, L.YUANFANG, X., USING PROSODIC AND CONVERSATIONAL FEATURES FOR HIGH-PERFORMANCE SPEAKER RECOGNITION: REPORT FROM JHU WS'02, 2003 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2003. PROCEEDINGS. (ICASSP '03, May 2012 (2012-05-01)
ref_type: npl
severity: background
ref_id: - ASR, F. T.FAZLY, A.AZIMIFAR, Z.: "In International Conference on Trustworthy Computing and Services", 2010, SPRINGER, article "Based on support vector and word features new word discovery research", pages: 287 - 294
ref_type: npl
severity: background
ref_id: - "A computational modeling approach", IN PROCEEDINGS OF THE ANNUAL MEETING OF THE COGNITIVE SCIENCE SOCIETY, vol. 32, no. 32
ref_type: npl
severity: background
ref_id: US2018096102A1
ref_type: patent
severity: background
ref_id: - MAMEDE NUNO ET AL: "Automated anonymization of text documents", 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 24 July 2016 (2016-07-24), pages 1287 - 1294, XP033006837, DOI: 10.1109/CEC.2016.7743936
ref_type: npl
severity: background
ref_id: - NUNO MAMEDE ET AL: "STRING: An Hybrid Statistical and Rule-Based Natural Language Processing Chain for Portuguese", 17 April 2012 (2012-04-17), XP055718806, Retrieved from the Internet <URL:https://www.inesc-id.pt/publications/8578/pdf> [retrieved on 20200728]
ref_type: npl
severity: background
ref_id: - MCDONALD GRAHAM ET AL: "Towards a Classifier for Digital Sensitivity Review", 13 April 2014, ANNUAL INTERNATIONAL CONFERENCE ON THE THEORY AND APPLICATIONS OF CRYPTOGRAPHIC TECHNIQUES, EUROCRYPT 2018; [LECTURE NOTES IN COMPUTER SCIENCE; LECT.NOTES COMPUTER], SPRINGER, BERLIN, HEIDELBERG, PAGE(S) 500 - 506, ISBN: 978-3-642-17318-9, XP047267008
ref_type: npl
severity: background
ref_id: - BENJAMIN WILLIAM MEDLOCK: "Investigating classification for natural language processing tasks", 1 January 2008 (2008-01-01), XP055492341, Retrieved from the Internet <URL:https://pdfs.semanticscholar.org/7dda/da6a7db6cdd22a93a1a415164b31ac00be79.pdf?_ga=2.60786986.602609875.1531412411-65692255.1513267587> [retrieved on 20180712]
ref_type: npl
severity: background
ref_id: US2018096102
ref_type: patent
severity: background
ref_id: - MAMEDE NUNO ET AL, "Automated anonymization of text documents", 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, (20160724), doi:10.1109/CEC.2016.7743936, pages 1287 - 1294, XP033006837 [X] 1-4,6-11,13 * abstract * * page  1  - page  4 * [I] 5,12,14,15
ref_type: npl
severity: novelty_destroying
ref_id: - Nuno Mamede ET AL, "STRING: An Hybrid Statistical and Rule-Based Natural Language Processing Chain for Portuguese", (20120417), URL: https://www.inesc-id.pt/publications/8578/pdf, (20200728), XP055718806 [A] 1-15 * page  1  - page  2 *
ref_type: npl
severity: background
ref_id: - MCDONALD GRAHAM ET AL, Towards a Classifier for Digital Sensitivity Review, ANNUAL INTERNATIONAL CONFERENCE ON THE THEORY AND APPLICATIONS OF CRYPTOGRAPHIC TECHNIQUES, EUROCRYPT 2018; [LECTURE NOTES IN COMPUTER SCIENCE; LECT.NOTES COMPUTER], SPRINGER, BERLIN, HEIDELBERG, PAGE(S) 500 - 506, (20140413), ISBN 978-3-642-17318-9, XP047267008 [A] 1-15 * abstract * * page  1  - page  6 *
ref_type: npl
severity: background
ref_id: - Benjamin William Medlock, "Investigating classification for natural language processing tasks", (20080101), URL: https://pdfs.semanticscholar.org/7dda/da6a7db6cdd22a93a1a415164b31ac00be79.pdf?_ga=2.60786986.602609875.1531412411-65692255.1513267587, (20180712), XP055492341 [A] 1-15 * abstract * * page  99  - page  123 *
ref_type: npl
severity: background
Final disposition: pending (2020-10-21)