Preserving privacy in geo-targeted advertising

Ian Nathan Anggono, Hamed Haddadi, Abdelberi Chaabane, Mohamed Ali Kaafar

Research output: Contribution to conferencePaperpeer-review


Targeted advertising has inherent privacy risks: ad providers aim to maximize the information inferred about the users in order to increase their click-through ratio. This in turn leads to long-term privacy risks for the users as their information is traded among ad agencies and unknown third parties. In this paper we focus on the privacy associated with information such as location and income, and their relationships with the ads served to users. We first show
the possibility for an attacker to use topic modelling and machine learning techniques on ads served to a user as a means to accurately infer their location and income band within a city. We then attempt to reduce this risk of inference using obfuscation, or hiding in plain sight. The idea is to hide the targeted ads, not by relying on encryption, rather by producing just enough noise such that an attacker cannot distinguish between the actual ads served and the ads which
are just noise. Our results are promising and demonstrate that efforts such as TrackMeNot can help advertising and users achieve a balance between targeted advertising and location privacy exposure.
Original languageEnglish
Number of pages6
Publication statusPublished - 2016
Externally publishedYes
EventTargetAd: 2nd International Workshop on Ad Targeting at Scale in conjunction with The 9th ACM International Conference on Web Search and Data Mining (WSDM 2016) - San Francisco, United States
Duration: 22 Feb 201625 Feb 2016


Abbreviated titleTargetAd 2016
Country/TerritoryUnited States
CitySan Francisco


  • advertisement
  • privacy
  • machine learning


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