Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Audience selection for on-line brand advertising: privacy-friendly social network targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive privacy via the median mechanism
Proceedings of the forty-second ACM symposium on Theory of computing
Privacy Regulation and Online Advertising
Management Science
Predictive client-side profiles for personalized advertising
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
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Privacy is a major concern for Internet users and Internet policy regulators. Privacy violations usually entail either sharing Personally Identifying Information (PII) or non-PII information such as a site visitor's behavior on a website. On the other hand, Internet advertising through behavioral targeting is an important part of the Internet ecosystem, as it provides users more relevant information and enables content/data providers to provide free services to end users. In order to achieve effective behavioral targeting, it is desirable for the advertisers to access a set of users with the targeted behaviors. A key question is how should data flow from a provider (e.g. publisher) to a third party advertiser to achieve effective behavioral targeting, all while without directly sharing exact user behavior data. This paper attempts to answer this question and proposes a privacy preserving technique for behavioral targeting that does not entail a drastic reduction in advertising effectiveness. When behavioral targeting data is transferred to an advertiser, we use a smart, data mining-based noise injection method that perturbs the results (a set of users meeting specified criteria) by carefully adding noisy data points that maintain a high level of performance. Upon receiving the data, the advertiser cannot distinguish accurate data points adhering to specifications, versus noisy data, which does not meet the specifications. Using data from a major US top Online Travel Agent (OTA), we evaluated the proposed technique for location-based behavioral targeting, whereby advertisers run data campaigns targeting travelers for specific destinations. Our experimental results demonstrate that such data campaigns obtain results that enhance or preserve user privacy while maintaining a high level of targeting performance.