Adnostic: Privacy Preserving Targeted Advertising

By V. Toubiana, Arvind Narayanan, Dan Boneh, Helen Nissenbaum, Solon Barocas. Network and Distributed System Security Symposium, 2010

359 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2e7abf1e48588dd07f6aaba750b83ec8fbbb70be

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Citations in the corpus

Towards Statistical Queries over Distributed Private User Data (2012, background): “This “user-owned and operated” principle has already been reflected in commercial and research efforts on various types of user data, such as individuals’ healthcare data [3], time-series data [45, 49], and behavioral data used for advertising [7, 31, 51].”
CloneCloud: elastic execution between mobile device and cloud (2011, methodology): “The privacy-preserving targeted-advertising application uses behavioral tracking across web-sites to infer the user’s preferences, and selects ads according to a resulting model; by doing this tracking at the user’s device, privacy can be protected (see Adnostic [Toubiana 2010]).”
SplitX: high-performance private analytics (2013, background): “Concurrent to these developments, there has been active research on ways to allow behavioral advertising [6,15,19,32] or analytics [4,9,13,20,26,28] while still protecting user privacy.”
Third-Party Web Tracking: Policy and Technology (2012, methodology): “Like Privad, Adnostic [100] uses client-based functionality to perform ad selection, but it eliminates anonymizing proxies at the cost of less precise ad targeting.”
Detecting and Defending Against Third-Party Tracking on the Web (2012, background): “Further afield, a number of researchers [5, 7, 23] have tackled the problem of privacy-preserving targeted advertising and other personalized content, attempting to find a middle ground that balances the values of users, websites, and advertisers or other content providers.”
Scalable and Private Media Consumption with Popcorn (2016, background): “, prior works [13, 22, 53, 96] have addressed them in different contexts).”
Privacy Violations Using Microtargeted Ads: A Case Study (2010, background): “The works of [46] and [17] propose systems that perform profiling, ad selection and”
Protecting privacy when mining and sharing user data (2012, background): “The works of [180] and [74] propose systems that perform profiling, ad selection, and targeting on the client’s (user’s) side and use cryptographic techniques to ensure accurate accounting.”
A Protocol for Privately Reporting Ad Impressions at Scale (2016, methodology): “Voting systems such as Adnostic [42] transmit encrypted vectors that contain a 1 in each position corresponding to a viewed advertisement, and a 0 in every other position, allowing an honest-but-curious central party to homomorphically sum the ciphertexts into a single aggregate ciphertext that can…”
A Roadmap for a Rigorous Science of Interpretability (2017, background): “Some areas, such as the fairness [Hardt et al., 2016] and privacy [Toubiana et al., 2010, Dwork et al., 2012, Hardt and Talwar, 2010] the research communities have formalized their criteria, and these formalizations have allowed for a blossoming of rigorous research in these fields (without the…”
Unsafe exposure analysis of mobile in-app advertisements (2012, background): “Adnostic: Privacy Preserving Targeted Advertising.”
Towards A Rigorous Science of Interpretable Machine Learning (2017, background): “Some areas, such as the fairness [Hardt et al., 2016] and privacy [Toubiana et al., 2010, Dwork et al., 2012, Hardt and Talwar, 2010] the research communities have formalized their criteria, and these formalizations have allowed for a blossoming of rigorous research in these ﬁelds (without the need…”
Human-Data Interaction: The Human Face of the Data-Driven Society (2014, unknown): “…should adopt privacy mechanisms such as differential privacy (Dwork, 2006), privacy-preserving proﬁling and advertising schemes such as Adnostic (Toubiana et al., 2010), Mo-biAd (Haddadi et al., 2010), or PrivAd (Guha et al., 2009), or privacy metaphors (Adams et al., 2010; Kapadia et al.,…”
Non-tracking web analytics (2012, unknown): “posing user information [40, 51], for this paper we assume that the data aggregator requires only aggregate data.”