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#DontTweetThis: Scoring Private Information in Social Networks

. Liu and E. Terzi. A framework for computing the privacy scores of users in online social networks. ACM Transactions on Knowledge Discovery from Data , 5(1), 2010. [46] W. Liu and D. Ruths. What’s in a name? using first names as features for gender inference in twitter. In AAAI spring symposium: Analyzing microtext , volume 13, page 01, 2013. [47] B. Luo and D. Lee. On protecting private information in social networks: a proposal. In IEEE ICME Workshop of M3SN . IEEE, 2009. [48] A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam

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NoMoAds: Effective and Efficient Cross-App Mobile Ad-Blocking


Although advertising is a popular strategy for mobile app monetization, it is often desirable to block ads in order to improve usability, performance, privacy, and security. In this paper, we propose NoMoAds to block ads served by any app on a mobile device. NoMoAds leverages the network interface as a universal vantage point: it can intercept, inspect, and block outgoing packets from all apps on a mobile device. NoMoAds extracts features from packet headers and/or payload to train machine learning classifiers for detecting ad requests. To evaluate NoMoAds, we collect and label a new dataset using both EasyList and manually created rules. We show that NoMoAds is effective: it achieves an F-score of up to 97.8% and performs well when deployed in the wild. Furthermore, NoMoAds is able to detect mobile ads that are missed by EasyList (more than one-third of ads in our dataset). We also show that NoMoAds is efficient: it performs ad classification on a per-packet basis in real-time. To the best of our knowledge, NoMoAds is the first mobile ad-blocker to effectively and efficiently block ads served across all apps using a machine learning approach.

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SeaGlass: Enabling City-Wide IMSI-Catcher Detection

:// . Accessed: 2017-03-14. [29] SR Labs. Snoopsnitch. . Accessed: 2017-03-14. [30] SR Labs. Snoopsnitch - IMSI Catcher Score. . [31] Unwired labs. . Accessed: 2017-03-14. [32] F. van den Broek, R. Verdult, and J. de Ruiter. Defeating imsi catchers. In Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security , CCS ’15, pages 340–351, New York, NY, USA, 2015. ACM. [33

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I never signed up for this! Privacy implications of email tracking

traffic. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services , pages 361–374. ACM, 2016. [35] Franziska Roesner, Tadayoshi Kohno, and David Wetherall. Detecting and defending against third-party tracking on the web. In Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation , pages 12–12. USENIX Association, 2012. [36] scikit-learn. Jaccard Similarity Score. . Online; accessed

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Automated Experiments on Ad Privacy Settings
A Tale of Opacity, Choice, and Discrimination

near parity - for now: Despite gains, many see roadblocks ahead,” 2013. [32] T. Z. Zarsky, “Understanding discrimination in the scored society,” Washington Law Review, vol. 89, pp. 1375-1412, 2014. [33] R. S. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork, “Learning fair representations,” in Proceedings of the 30th International Conference on Machine Learning, ser. JMLR: W&CP, vol. 28., 2013, pp. 325-333. [34] Adgooroo, “Adwords cost per click rises 26% between 2012 and 2014,” http

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Location Privacy for Rank-based Geo-Query Systems

IEEE International Conference on Computer Communications , pages 1017–1025, 2015. [23] B. O’Clair, D. Egnor, and L. E. Greenfield. Scoring local search results based on location prominence, 2011. US Patent 8,046,371. [24] D. M. W. Powers. Applications and Explanations of Zipf’s Law. In Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning , pages 151–160, 1998. [25] J. Reed and B. C. Pierce. Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy. In Proceedings

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A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X

for twitter sentiment classification. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , volume 1, pages 1555–1565, 2014. [46] L.-C. Yu, J. Wang, K. R. Lai, and X. Zhang. Refining word embeddings using intensity scores for sentiment analysis. In IEEE/ACM Transactions on Audio, Speech, and Language Processing , 2018.

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Private Evaluation of Decision Trees using Sublinear Cost

IJCAI , pages 764–769, San Francisco, CA, USA, 1991. Morgan Kaufmann Publishers Inc. [15] D. Chaum, C. Crépeau, and I. Damgard. Multiparty unconditionally secure protocols. In STOC , pages 11–19, 1988. [16] M. D. Cock, R. Dowsley, C. Horst, R. Katti, A. C. A. Nascimento, S. C. Newman, and W. Poon. Efficient and private scoring of decision trees, support vector machines and logistic regression models based on pre-computation. IACR Cryptology ePrint Archive , 2016:736, 2016. [17] R. Cramer, I. Damgård, and J. B. Nielsen. Multiparty computation from

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Privately Evaluating Decision Trees and Random Forests

. [47] J. Katz and L. Malka. Constant-round private function evaluation with linear complexity. In ASIACRYPT, pages 556-571, 2011. [48] J. Kilian. Founding cryptography on oblivious transfer. In STOC, pages 20-31, 1988. [49] H. C. Koh, W. C. Tan, and C. P. Goh. A two-step method to construct credit scoring models with data mining techniques. International Journal of Business and Information, 1:96-118, 2006. [50] V. Kolesnikov, A.-R. Sadeghi, and T. Schneider. Improved garbled circuit building blocks and applications to

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My Genome Belongs to Me: Controlling Third Party Computation on Genomic Data

:// . [23] Peter JA Cock, Christopher J Fields, Naohisa Goto, Michael L Heuer, and Peter M Rice. The sanger fastq file format for sequences with quality scores, and the solexa/illumina fastq variants. Nucleic acids research , 38(6):1767–1771, 2010. [24] Francis S Collins, Lisa D Brooks, and Aravinda Chakravarti. A dna polymorphism discovery resource for research on human genetic variation. Genome research , 8(12):1229–1231, 1998. [25] Ronald Cramer and Victor Shoup. Universal hash proofs and a paradigm for adaptive chosen ciphertext secure

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