TY - GEN
T1 - Betrayed by your ads! Reconstructing user profiles from targeted ads
AU - Castelluccia, Claude
AU - Kaafar, Mohamed Ali
AU - Tran, Minh Dung
PY - 2012
Y1 - 2012
N2 - In targeted (or behavioral) advertising, users' behaviors are tracked over time in order to customize served ads to their interests. This creates serious privacy concerns since for the purpose of profiling, private information is collected and centralized by a limited number of companies. Despite claims that this information is secure, there is a potential for this information to be leaked through the customized services these companies are offering. In this paper, we show that targeted ads expose users' private data not only to ad providers but also to any entity that has access to users' ads. We propose a methodology to filter targeted ads and infer users' interests from them. We show that an adversary that has access to only a small number of websites containing Google ads can infer users' interests with an accuracy of more than 79% (Precision) and reconstruct as much as 58% of a Google Ads profile in general (Recall). This paper is, to our knowledge, the first work that identifies and quantifies information leakage through ads served in targeted advertising.
AB - In targeted (or behavioral) advertising, users' behaviors are tracked over time in order to customize served ads to their interests. This creates serious privacy concerns since for the purpose of profiling, private information is collected and centralized by a limited number of companies. Despite claims that this information is secure, there is a potential for this information to be leaked through the customized services these companies are offering. In this paper, we show that targeted ads expose users' private data not only to ad providers but also to any entity that has access to users' ads. We propose a methodology to filter targeted ads and infer users' interests from them. We show that an adversary that has access to only a small number of websites containing Google ads can infer users' interests with an accuracy of more than 79% (Precision) and reconstruct as much as 58% of a Google Ads profile in general (Recall). This paper is, to our knowledge, the first work that identifies and quantifies information leakage through ads served in targeted advertising.
KW - information leakage
KW - privacy
KW - Targeted Advertising
UR - http://www.scopus.com/inward/record.url?scp=84864229821&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-31680-7_1
DO - 10.1007/978-3-642-31680-7_1
M3 - Conference proceeding contribution
AN - SCOPUS:84864229821
SN - 9783642316791
T3 - Lecture Notes in Computer Science
SP - 1
EP - 17
BT - Privacy Enhancing Technologies
A2 - Fischer-Hubner, Simone
A2 - Wright, Matthew
PB - Springer, Springer Nature
CY - Berlin
T2 - 12th International Symposium on Privacy Enhancing Technologies, PETS 2012
Y2 - 11 July 2012 through 13 July 2012
ER -