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Analysis of Risks to Data Privacy for Family Units in Many Countries

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Advances in Human Factors in Robots, Unmanned Systems and Cybersecurity (AHFE 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 268))

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Abstract

Ground-breaking research by Sweeney a number of years ago demonstrated the vulnerability of personal information that can be relatively easily discovered, allowing a malicious attacker or hacker to be able to recover sensitive information about an individual. In particular, the Sweeney research showed that close to 90% of the individuals in the United States can be identified uniquely using only three easily discoverable data points: postal code, gender, and birthdate including year. Our current research has shown that in most United Nation member countries, including the United States, almost 90% of family units at the same residence can be identified uniquely using only two easily found data points: postal code and birthdate including year of one family member.

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References

  1. Sweeney, L: Simple demographics often identify people uniquely. Data Privacy Working Paper 3. Carnegie Mellon University, Pittsburgh (2000)

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  2. Patterson, W., Winston-Proctor, C.E.: An international extension of Sweeney’s data privacy research. In: Ahram, T., Karwowski, W. (eds.) Advances in Human Factors in Cybersecurity. Advances in Intelligent Systems and Computing, vol. 960, pp. 28–37. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-20488-4_3

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  3. United Nations. https://population.un.org/wpp/Download/Standard/Population/

  4. Mean International Wealth Index (IWI) score of region - Area Database - Global Data Lab. Globaldatalab.org

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  5. Wikipedia. https://en.wikipedia.org/wiki/List_of_countries_by_life_expectancy

  6. Wikipedia. https://en.wikipedia.org/wiki/List_of_postal_codes

  7. International Standards Organization (ISO). https://www.iso.org/iso-3166-country-codes.html.

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Patterson, W. (2021). Analysis of Risks to Data Privacy for Family Units in Many Countries. In: Zallio, M., Raymundo Ibañez, C., Hernandez, J.H. (eds) Advances in Human Factors in Robots, Unmanned Systems and Cybersecurity. AHFE 2021. Lecture Notes in Networks and Systems, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-79997-7_27

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