eprintid: 10182351
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/18/23/51
datestamp: 2024-01-30 14:10:26
lastmod: 2024-01-30 14:10:26
status_changed: 2024-01-30 14:10:26
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Gervais, Arthur
creators_name: Shokri, Reza
creators_name: Singla, Adish
creators_name: Capkun, Srdjan
creators_name: Lenders, Vincent
title: Quantifying Web-Search Privacy
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Web Search; Privacy; Obfuscation; Quantification Framework; Query Privacy; Semantic Privacy; Machine Learning
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Web search queries reveal extensive information about users’
personal lives to the search engines and Internet eavesdroppers. Obfuscating search queries through adding dummy
queries is a practical and user-centric protection mechanism
to hide users’ search intentions and interests. Despite few
such obfuscation methods and tools, there is no generic
quantitative methodology for evaluating users’ web-search
privacy. In this paper, we provide such a methodology. We
formalize adversary’s background knowledge and attacks,
the users’ privacy objectives, and the algorithms to evaluate effectiveness of query obfuscation mechanisms. We
build upon machine-learning algorithms to learn the linkability between user queries. This encompasses the adversary’s knowledge about the obfuscation mechanism and the
users’ web-search behavior. Then, we quantify privacy of
users with respect to linkage attacks. Our generic attack can
run against users for which the adversary does not have any
background knowledge, as well as for the cases where some
prior queries from the target users are already observed. We
quantify privacy at the query level (the link between user’s
queries) and the semantic level (user’s topics of interest). We
design a generic tool that can be used for evaluating generic
obfuscation mechanisms, and users with different web search
behavior. To illustrate our approach in practice, we analyze
and compare privacy of users for two example obfuscation
mechanisms on a set of real web-search logs.
date: 2014-11-03
date_type: published
publisher: Association for Computing Machinery (ACM)
official_url: https://dl.acm.org/doi/10.1145/2660267.2660367
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2112541
doi: 10.1145/2660267.2660367
lyricists_name: Gervais, Arthur
lyricists_id: AGERV21
actors_name: Gervais, Arthur
actors_id: AGERV21
actors_role: owner
full_text_status: public
pres_type: paper
publication: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security
place_of_pub: Scottsdale, AZ, USA
pagerange: 966-977
event_title: CCS'14: 2014 ACM SIGSAC Conference on Computer and Communications Security
book_title: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security
citation:        Gervais, Arthur;    Shokri, Reza;    Singla, Adish;    Capkun, Srdjan;    Lenders, Vincent;      (2014)    Quantifying Web-Search Privacy.                     In:  Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security.  (pp. pp. 966-977).  Association for Computing Machinery (ACM): Scottsdale, AZ, USA.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10182351/1/ccs_gervais.pdf