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

Kevin He; Xiaosheng Mu

doi:10.1145/2600057.2602841

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

Kevin He, Xiaosheng Mu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 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 language	English (US)
Title of host publication	EC 2014 - Proceedings of the 15th ACM Conference on Economics and Computation
Publisher	Association for Computing Machinery
Pages	949-966
Number of pages	18
ISBN (Print)	9781450325653
DOIs	https://doi.org/10.1145/2600057.2602841
State	Published - 2014
Externally published	Yes
Event	15th ACM Conference on Economics and Computation, EC 2014 - Palo Alto, CA, United States Duration: Jun 8 2014 → Jun 12 2014

Publication series

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

Other

Other	15th ACM Conference on Economics and Computation, EC 2014
Country/Territory	United States
City	Palo Alto, CA
Period	6/8/14 → 6/12/14

All Science Journal Classification (ASJC) codes

Computer Science (miscellaneous)

Keywords

differential privacy
network game
recommender system

Access to Document

10.1145/2600057.2602841

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

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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.",

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author = "Kevin He and Xiaosheng Mu",

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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. EC 2014 - Proceedings of the 15th ACM Conference on Economics and Computation, Association for Computing Machinery, pp. 949-966, 15th ACM Conference on Economics and Computation, EC 2014, Palo Alto, CA, United States, 6/8/14. https://doi.org/10.1145/2600057.2602841

Differentially private and incentive compatible recommendation system for the adoption of network goods. / He, Kevin; Mu, Xiaosheng.
EC 2014 - Proceedings of the 15th ACM Conference on Economics and Computation. Association for Computing Machinery, 2014. p. 949-966 (EC 2014 - Proceedings of the 15th ACM Conference on Economics and Computation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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ER -

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

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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