Introducing Privacy in Current Web Search Engines

During the last few years, the technological progress in collecting, storing and processing a large quantity of data for a reasonable cost has raised serious privacy issues. Privacy concerns many areas, but is especially important in frequently used services like search engines (e.g., Google, Bing, Yahoo!). These services allow users to retrieve relevant content on the Internet by exploiting their personal data. In this context, developing solutions to enable users to use these services in a privacy-preserving way is becoming increasingly important. In this thesis, we introduce SimAttack an attack against existing protection mechanism to query search engines in a privacy-preserving way. This attack aims at retrieving the original user query. We show with this attack that three representative state-of-the-art solutions do not protect the user privacy in a satisfactory manner. We therefore develop PEAS a new protection mechanism that better protects the user privacy. This solution leverages two types of protection: hiding the user identity (with a succession of two nodes) and masking users' queries (by combining them with several fake queries). To generate realistic fake queries, PEAS exploits previous queries sent by the users in the system. Finally, we present mechanisms to identify sensitive queries. Our goal is to adapt existing protection mechanisms to protect sensitive queries only, and thus save user resources (e.g., CPU, RAM). We design two modules to identify sensitive queries. By deploying these modules on real protection mechanisms, we establish empirically that they dramatically improve the performance of the protection mechanisms.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Universität Passau

Year

2017

Author dc:creator

• Petit, Albin

Contributors dc:contributor

• Brunie, Lionel
• Granitzer, Michael

• Privacy
• Search Engine
• Unlinkability
• Indistinguishability

Statement dc:rights

• CC by: Creative Commons - Namensnennung

Repository record source_url

https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/465

OAI identifier oai:identifier

oai:kobv.de-opus4-uni-passau:465