Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geospatial Technologies

@article{Iyer2021SpatialKA,
  title={Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geospatial Technologies},
  author={Rohan Iyer and Regina Rex and Kevin P. McPherson and Darshan Gandhi and Aryan Mahindra and Abhishek Singh and Ramesh Raskar},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.02556}
}
There is a growing need for spatial privacy considerations in the many geo-spatial technologies that have been created as solutions for COVID-19-related issues. Although effective geo-spatial technologies have already been rolled out, most have significantly sacrificed privacy for utility. In this paper, we explore spatial k-anonymity, a privacy-preserving method that can address this unnecessary tradeoff by providing the best of both privacy and utility. After evaluating its past implications… 

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References

SHOWING 1-10 OF 40 REFERENCES

Geomasking sensitive health data and privacy protection: an evaluation using an E911 database

TLDR
This work evaluated the performance of donut geomasking in Orange County, North Carolina and found Census block groups in mixed-use areas with high population distribution heterogeneity were the most likely to have privacy protection below selected criteria.

Geo-indistinguishability: differential privacy for location-based systems

The growing popularity of location-based systems, allowing unknown/untrusted servers to easily collect huge amounts of information regarding users' location, has recently started raising serious

A reciprocal framework for spatial K-anonymity

PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing

TLDR
This work advocates for a third-party free approach to assisted mobile contact tracing, because such an approach mitigates the security and privacy risks of requiring a trusted third party.

Musings on privacy issues in health research involving disaggregate geographic data about individuals

TLDR
A novel, 'one stop shop' case-based reasoning framework is proposed to streamline the provision of clear and individualised guidance for the design and approval of new research projects (involving geographical identifiers about individuals), including crisp recommendations on which specific privacy-preserving solutions and approaches would be suitable in each case.

Too Much Information: Assessing Privacy Risks of Contact Trace Data Disclosure on People With COVID-19 in South Korea

TLDR
The role of “identifiability” in contact tracing is discussed to provide new directions for minimizing disclosure of privacy infringing information and strike a balance between one's privacy and the public benefits with data disclosure.

Application of Information Technology: A Context-sensitive Approach to Anonymizing Spatial Surveillance Data: Impact on Outbreak Detection

TLDR
A population-density-based Gaussian spatial blurring markedly decreases the ability to identify individuals in a data set while only slightly decreasing the performance of a standardly used outbreak detection tool.

Achieving k-Anonymity Privacy Protection Using Generalization and Suppression

  • L. Sweeney
  • Computer Science
    Int. J. Uncertain. Fuzziness Knowl. Based Syst.
  • 2002
TLDR
This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity and shows that Datafly can over distort data and µ-Argus can additionally fail to provide adequate protection.

Providing K-Anonymity in location based services

TLDR
This work surveys recent advancements for the offering of K-anonymity in LBSs and presents some of the most prevalent approaches, which heavily depend on a trusted server component that acts as an intermediate between the end user and the service provider to preserve the anonymity of the former entity.

L-diversity: privacy beyond k-anonymity

TLDR
This paper shows with two simple attacks that a \kappa-anonymized dataset has some subtle, but severe privacy problems, and proposes a novel and powerful privacy definition called \ell-diversity, which is practical and can be implemented efficiently.