research-article

Privacy-Preserving Crowd-Sourcing of Web Searches with Private Data Donor

Authors:

Vincent Primault,

Vasileios Lampos,

Emiliano De CristofaroAuthors Info & Claims

WWW '19: The World Wide Web Conference

Pages 1487 - 1497

https://doi.org/10.1145/3308558.3313474

Published: 13 May 2019 Publication History

Abstract

Search engines play an important role on the Web, helping users find relevant resources and answers to their questions. At the same time, search logs can also be of great utility to researchers. For instance, a number of recent research efforts have relied on them to build prediction and inference models, for applications ranging from economics and marketing to public health surveillance. However, companies rarely release search logs, also due to the related privacy issues that ensue, as they are inherently hard to anonymize. As a result, it is very difficult for researchers to have access to search data, and even if they do, they are fully dependent on the company providing them. Aiming to overcome these issues, this paper presents Private Data Donor (PDD), a decentralized and private-by-design platform providing crowd-sourced Web searches to researchers. We build on a cryptographic protocol for privacy-preserving data aggregation, and address a few practical challenges to add reliability into the system with regards to users disconnecting or stopping using the platform. We discuss how PDD can be used to build a flu monitoring model, and evaluate the impact of the privacy-preserving layer on the quality of the results. Finally, we present the implementation of our platform, as a browser extension and a server, and report on a pilot deployment with real users.

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Cited By

Shen YYu BLai SYuan XSun SLiu JNepal S(2022)OblivSend: Secure and Ephemeral File Sharing Services with Oblivious Expiration ControlInformation Security10.1007/978-3-031-22390-7_17(269-289)Online publication date: 18-Dec-2022
https://dl.acm.org/doi/10.1007/978-3-031-22390-7_17
Azad MPerera CBag SBarhamgi MHao F(2020)Privacy-preserving Crowd-sensed Trust Aggregation in the User-centeric Internet of People NetworksACM Transactions on Cyber-Physical Systems10.1145/33908605:1(1-24)Online publication date: 30-Dec-2020
https://dl.acm.org/doi/10.1145/3390860
Meurisch CBayrak BMühlhäuser M(2020)Privacy-preserving AI Services Through Data DecentralizationProceedings of The Web Conference 202010.1145/3366423.3380106(190-200)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380106

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cover image ACM Other conferences

WWW '19: The World Wide Web Conference

May 2019

3620 pages

ISBN:9781450366748

DOI:10.1145/3308558

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 May 2019

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WWW '19: The Web Conference

May 13 - 17, 2019

CA, San Francisco, USA

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Cited By

Shen YYu BLai SYuan XSun SLiu JNepal S(2022)OblivSend: Secure and Ephemeral File Sharing Services with Oblivious Expiration ControlInformation Security10.1007/978-3-031-22390-7_17(269-289)Online publication date: 18-Dec-2022
https://dl.acm.org/doi/10.1007/978-3-031-22390-7_17
Azad MPerera CBag SBarhamgi MHao F(2020)Privacy-preserving Crowd-sensed Trust Aggregation in the User-centeric Internet of People NetworksACM Transactions on Cyber-Physical Systems10.1145/33908605:1(1-24)Online publication date: 30-Dec-2020
https://dl.acm.org/doi/10.1145/3390860
Meurisch CBayrak BMühlhäuser M(2020)Privacy-preserving AI Services Through Data DecentralizationProceedings of The Web Conference 202010.1145/3366423.3380106(190-200)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380106

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