Privado

Author:

Boshrooyeh Sanaz Taheri1,Küpçü Alptekin1ORCID,Özkasap Öznur1

Affiliation:

1. Koç University, İstanbul, Turkey

Abstract

Online Social Networks (OSNs) offer free storage and social networking services through which users can communicate personal information with one another. The personal information of the users collected by the OSN provider comes with privacy problems when being monetized for advertising purposes. To protect user privacy, existing studies propose utilizing data encryption that immediately prevents OSNs from monetizing users data and hence leaves secure OSNs with no convincing commercial model. To address this problem, we propose Privado as a privacy-preserving group-based advertising mechanism to be integrated into secure OSNs to re-empower monetizing ability. Privado is run by N servers, each provided by an independent provider. User privacy is protected against an active malicious adversary controlling N − 1 providers, all the advertisers, and a large fraction of the users. We base our design on the group-based advertising notion to protect user privacy, which is not possible in the personalized variant. Our design also delivers advertising transparency; the procedure of identifying target customers is operated solely by the OSN servers without getting users and advertisers involved. We carry out experiments to examine the advertising running time under various number of servers and group sizes. We also argue about the optimum number of servers with respect to user privacy and advertising running time.

Funder

Türkiye Bilimler Akademisi

TÜBITAK

Royal Society

EU Cost Action

Publisher

Association for Computing Machinery (ACM)

Subject

Safety, Risk, Reliability and Quality,General Computer Science

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Security and Privacy of Customer Data as an Element Creating the Image of the Company;Management Systems in Production Engineering;2022-05-19

2. A Trust based Privacy Providing Model for Online Social Networks;Online Social Networks and Media;2021-07

3. Opera: Scalable Simulator for Distributed Systems;IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS);2021-05-10

4. Targeted Advertising That Protects the Privacy of Social Networks Users;HUM-CENT COMPUT INFO;2021

5. Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey;IEEE Access;2021

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