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Effects of Privacy-Inducing Noise on Welfare and Influence of Referendum Systems

Authors Suat Evren , Praneeth Vepakomma

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  • Security and privacy → Economics of security and privacy
  • Applied computing → Economics
  • Theory of computation → Dynamic programming
Keywords
  • Welfare
  • influence
  • social choice functions
  • differential privacy
  • randomized response

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Abstract

Social choice functions help aggregate individual preferences while differentially private mechanisms provide formal privacy guarantees to release answers of queries operating on sensitive data. However, preserving differential privacy requires introducing noise to the system, and therefore may lead to undesired byproducts. Does an increase in the level of privacy for releasing the outputs of social choice functions increase or decrease the level of influence and welfare, and at what rate? In this paper, we mainly address this question in more precise terms in a referendum setting with two candidates when the celebrated randomized response mechanism is used. We show that the level of privacy is inversely proportional to society’s welfare and influence.

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Suat Evren and Praneeth Vepakomma. Effects of Privacy-Inducing Noise on Welfare and Influence of Referendum Systems. In 5th Symposium on Foundations of Responsible Computing (FORC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 295, pp. 1:1-1:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.FORC.2024.1

Author Details

Suat Evren
  • Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
Praneeth Vepakomma
  • MIT Institute for Data, Systems and Society (IDSS), Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
  • Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE

Funding

Research was sponsored by the United States Air Force Research Laboratory and the Department of the Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Department of the Air Force or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
  • Evren, Suat: Partially supported by MIT UROP Direct Funding and MIT Schwarzman SERC Scholarship.
  • Vepakomma, Praneeth: Researcher was partially supported by MBZUAI Startup Grant, ADIA Lab Fellowship, MIT Schwarzman SERC Scholarship and Meta PhD Research Fellowship.

Acknowledgements

We would like to thank Alex Pentland, Ramesh Raskar, and Ashwin Sah for helpful comments, support and feedback.

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