Have your cake and eat it too!: preserving privacy while achieving high behavioral targeting performance

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.