Literature DB >> 19581585

Predicting Social Security numbers from public data.

Alessandro Acquisti1, Ralph Gross.   

Abstract

Information about an individual's place and date of birth can be exploited to predict his or her Social Security number (SSN). Using only publicly available information, we observed a correlation between individuals' SSNs and their birth data and found that for younger cohorts the correlation allows statistical inference of private SSNs. The inferences are made possible by the public availability of the Social Security Administration's Death Master File and the widespread accessibility of personal information from multiple sources, such as data brokers or profiles on social networking sites. Our results highlight the unexpected privacy consequences of the complex interactions among multiple data sources in modern information economies and quantify privacy risks associated with information revelation in public forums.

Entities:  

Year:  2009        PMID: 19581585      PMCID: PMC2706270          DOI: 10.1073/pnas.0904891106

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  3 in total

Review 1.  Weaving technology and policy together to maintain confidentiality.

Authors:  L Sweeney
Journal:  J Law Med Ethics       Date:  1997 Summer-Fall       Impact factor: 1.718

2.  A method for constructing complete annual U.S. life tables.

Authors:  R N Anderson
Journal:  Vital Health Stat 2       Date:  2000

3.  A method for estimating year of birth using social security number.

Authors:  G Block; G M Matanoski; R S Seltser
Journal:  Am J Epidemiol       Date:  1983-09       Impact factor: 4.897

  3 in total
  11 in total

1.  Inferring social ties from geographic coincidences.

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Journal:  Proc Natl Acad Sci U S A       Date:  2010-12-08       Impact factor: 11.205

2.  Should Social Security numbers be replaced by modern, more secure identifiers?

Authors:  William E Winkler
Journal:  Proc Natl Acad Sci U S A       Date:  2009-07-06       Impact factor: 11.205

3.  How differential privacy will affect our understanding of health disparities in the United States.

Authors:  Alexis R Santos-Lozada; Jeffrey T Howard; Ashton M Verdery
Journal:  Proc Natl Acad Sci U S A       Date:  2020-05-28       Impact factor: 11.205

4.  Genetic data sharing and privacy.

Authors:  Marco D Sorani; John K Yue; Sourabh Sharma; Geoffrey T Manley; Adam R Ferguson
Journal:  Neuroinformatics       Date:  2015-01

Review 5.  Routes for breaching and protecting genetic privacy.

Authors:  Yaniv Erlich; Arvind Narayanan
Journal:  Nat Rev Genet       Date:  2014-05-08       Impact factor: 53.242

Review 6.  If you build it, they will come: unintended future uses of organised health data collections.

Authors:  Kieran C O'Doherty; Emily Christofides; Jeffery Yen; Heidi Beate Bentzen; Wylie Burke; Nina Hallowell; Barbara A Koenig; Donald J Willison
Journal:  BMC Med Ethics       Date:  2016-09-06       Impact factor: 2.652

Review 7.  Deep learning for misinformation detection on online social networks: a survey and new perspectives.

Authors:  Md Rafiqul Islam; Shaowu Liu; Xianzhi Wang; Guandong Xu
Journal:  Soc Netw Anal Min       Date:  2020-09-29

8.  Sociotechnical challenges and progress in using social media for health.

Authors:  Sean A Munson; Hasan Cavusoglu; Larry Frisch; Sidney Fels
Journal:  J Med Internet Res       Date:  2013-10-22       Impact factor: 5.428

9.  Internet Users' Valuation of Enhanced Data Protection on Social Media: Which Aspects of Privacy Are Worth the Most?

Authors:  Jasmin Mahmoodi; Jitka Čurdová; Christoph Henking; Marvin Kunz; Karla Matić; Peter Mohr; Maja Vovko
Journal:  Front Psychol       Date:  2018-08-22

10.  Diminishing personal information privacy weakens image concerns.

Authors:  Yohanes E Riyanto; Jianlin Zhang
Journal:  PLoS One       Date:  2020-04-27       Impact factor: 3.240

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