Qiaozhi Wang, Hao Xue, Fengjun Li, Dongwon Lee and Bo Luo
. 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.
 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.
 B. Luo and D. Lee. On protecting private information in social networks: a proposal. In IEEE ICME Workshop of M3SN . IEEE, 2009.
 A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam
Anastasia Shuba, Athina Markopoulou and Zubair Shafiq
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.
Peter Ney, Ian Smith, Gabriel Cadamuro and Tadayoshi Kohno
://www.skyhookwireless.com/ . Accessed: 2017-03-14.
 SR Labs. Snoopsnitch. https://opensource.srlabs.de/projects/snoopsnitch . Accessed: 2017-03-14.
 SR Labs. Snoopsnitch - IMSI Catcher Score. https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score .
 Unwired labs. https://unwiredlabs.com/ . Accessed: 2017-03-14.
 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.
Steven Englehardt, Jeffrey Han and Arvind Narayanan
traffic. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services , pages 361–374. ACM, 2016.
 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.
 scikit-learn. Jaccard Similarity Score. http://scikit-learn.org/stable/modules/generated/sklearn.metrics.jaccard_similarity_score.html . Online; accessed
Amit Datta, Michael Carl Tschantz and Anupam Datta
near parity - for now: Despite gains, many see roadblocks ahead,” 2013.
 T. Z. Zarsky, “Understanding discrimination in the scored society,” Washington Law Review, vol. 89, pp. 1375-1412, 2014.
 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. JMLR.org, 2013, pp. 325-333.
 Adgooroo, “Adwords cost per click rises 26% between 2012 and 2014,” http
Wisam Eltarjaman, Rinku Dewri and Ramakrishna Thurimella
IEEE International Conference on Computer Communications , pages 1017–1025, 2015.
 B. O’Clair, D. Egnor, and L. E. Greenfield. Scoring local search results based on location prominence, 2011. US Patent 8,046,371.
 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.
 J. Reed and B. C. Pierce. Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy. In Proceedings
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.
 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.
Anselme Tueno, Florian Kerschbaum and Stefan Katzenbeisser
IJCAI , pages 764–769, San Francisco, CA, USA, 1991. Morgan Kaufmann Publishers Inc.
 D. Chaum, C. Crépeau, and I. Damgard. Multiparty unconditionally secure protocols. In STOC , pages 11–19, 1988.
 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.
 R. Cramer, I. Damgård, and J. B. Nielsen. Multiparty computation from
David J. Wu, Tony Feng, Michael Naehrig and Kristin Lauter
 J. Katz and L. Malka. Constant-round private function evaluation with linear complexity. In ASIACRYPT, pages 556-571, 2011.
 J. Kilian. Founding cryptography on oblivious transfer. In STOC, pages 20-31, 1988.
 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.
 V. Kolesnikov, A.-R. Sadeghi, and T. Schneider. Improved garbled circuit building blocks and applications to
Dominic Deuber, Christoph Egger, Katharina Fech, Giulio Malavolta, Dominique Schröder, Sri Aravinda Krishnan Thyagarajan, Florian Battke and Claudia Durand
 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.
 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.
 Ronald Cramer and Victor Shoup. Universal hash proofs and a paradigm for adaptive chosen ciphertext secure