2017
1.
Wicker, Jörg; Kramer, Stefan
The Best Privacy Defense is a Good Privacy Offense: Obfuscating a Search Engine User’s Profile Journal Article
In: Data Mining and Knowledge Discovery, vol. 31, no. 5, pp. 1419-1443, 2017, ISSN: 1573-756X.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: adversarial learning, machine learning, personalized ads, privacy, reinforcement learning, search engines
@article{wicker2017best,
title = {The Best Privacy Defense is a Good Privacy Offense: Obfuscating a Search Engine User's Profile},
author = {J\"{o}rg Wicker and Stefan Kramer},
editor = {Kurt Driessens and Dragi Kocev and Marko Robnik-\v{S}ikonja and Myra Spiliopoulou},
url = {http://rdcu.be/tL0U},
doi = {10.1007/s10618-017-0524-z},
issn = {1573-756X},
year = {2017},
date = {2017-09-01},
journal = {Data Mining and Knowledge Discovery},
volume = {31},
number = {5},
pages = {1419-1443},
abstract = {User privacy on the internet is an important and unsolved problem. So far, no sufficient and comprehensive solution has been proposed that helps a user to protect his or her privacy while using the internet. Data are collected and assembled by numerous service providers. Solutions so far focused on the side of the service providers to store encrypted or transformed data that can be still used for analysis. This has a major flaw, as it relies on the service providers to do this. The user has no chance of actively protecting his or her privacy. In this work, we suggest a new approach, empowering the user to take advantage of the same tool the other side has, namely data mining to produce data which obfuscates the user’s profile. We apply this approach to search engine queries and use feedback of the search engines in terms of personalized advertisements in an algorithm similar to reinforcement learning to generate new queries potentially confusing the search engine. We evaluated the approach using a real-world data set. While evaluation is hard, we achieve results that indicate that it is possible to influence the user’s profile that the search engine generates. This shows that it is feasible to defend a user’s privacy from a new and more practical perspective.},
keywords = {adversarial learning, machine learning, personalized ads, privacy, reinforcement learning, search engines},
pubstate = {published},
tppubtype = {article}
}
User privacy on the internet is an important and unsolved problem. So far, no sufficient and comprehensive solution has been proposed that helps a user to protect his or her privacy while using the internet. Data are collected and assembled by numerous service providers. Solutions so far focused on the side of the service providers to store encrypted or transformed data that can be still used for analysis. This has a major flaw, as it relies on the service providers to do this. The user has no chance of actively protecting his or her privacy. In this work, we suggest a new approach, empowering the user to take advantage of the same tool the other side has, namely data mining to produce data which obfuscates the user’s profile. We apply this approach to search engine queries and use feedback of the search engines in terms of personalized advertisements in an algorithm similar to reinforcement learning to generate new queries potentially confusing the search engine. We evaluated the approach using a real-world data set. While evaluation is hard, we achieve results that indicate that it is possible to influence the user’s profile that the search engine generates. This shows that it is feasible to defend a user’s privacy from a new and more practical perspective.