Differentially private and incentive compatible recommendation system for the adoption of network goods

Kevin He, Xiaosheng Mu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

We study the problem of designing a recommendation system for network goods under the constraint of differential privacy. Agents living on a graph face the introduction of a new good and undergo two stages of adoption. The first stage consists of private, random adoptions. In the second stage, remaining non-adopters decide whether to adopt with the help of a recommendation system A. The good has network complimentarity, making it socially desirable for A to reveal the adoption status of neighboring agents. The designer's problem, however, is to find the socially optimal A that preserves privacy. We derive feasibility conditions for this problem and characterize the optimal solution.

Original languageEnglish (US)
Title of host publicationEC 2014 - Proceedings of the 15th ACM Conference on Economics and Computation
PublisherAssociation for Computing Machinery
Pages949-966
Number of pages18
ISBN (Print)9781450325653
DOIs
StatePublished - 2014
Externally publishedYes
Event15th ACM Conference on Economics and Computation, EC 2014 - Palo Alto, CA, United States
Duration: Jun 8 2014Jun 12 2014

Publication series

NameEC 2014 - Proceedings of the 15th ACM Conference on Economics and Computation

Other

Other15th ACM Conference on Economics and Computation, EC 2014
CountryUnited States
CityPalo Alto, CA
Period6/8/146/12/14

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)

Keywords

  • differential privacy
  • network game
  • recommender system

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  • Cite this

    He, K., & Mu, X. (2014). Differentially private and incentive compatible recommendation system for the adoption of network goods. In EC 2014 - Proceedings of the 15th ACM Conference on Economics and Computation (pp. 949-966). (EC 2014 - Proceedings of the 15th ACM Conference on Economics and Computation). Association for Computing Machinery. https://doi.org/10.1145/2600057.2602841