Mohsen Ghassemi
Rebecca N. Wright
ABSTRACT
In settings where data instances are generated sequentially or in streaming fashion, online learning algorithms can learn predictors using incremental training algorithms such as stochastic gradient descent. In some security applications such as training anomaly detectors, the data streams may consist of private information or transactions and the output of the learning algorithms may reveal information about the training data. Differential privacy is a framework for quantifying the privacy risk in such settings. This paper proposes two differentially private strategies to mitigate privacy risk when training a classifier for anomaly detection in an online setting. The first is to use a randomized active learning heuristic to screen out uninformative data points in the stream. The second is to use mini-batching to improve classifier performance. Experimental results show how these two strategies can trade off privacy, label complexity, and generalization performance.
- Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence. Dataset Shift in Machine Learning. MIT Press, Cambridge, MA, 2009. ISBN 0262170051, 9780262170055. Google Scholar
Digital Library
- Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM computing surveys (CSUR), 41 (3): 15:1--15:58, 2009. URL http://doi.acm.org/10.1145/1541880.1541882. Google ScholarDigital Library
- Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography, 2006. URL http://dx.doi.org/10.1007/11681878_14. Google ScholarDigital Library
- Raef Bassily, Adam Smith, and Abhradeep Thakurta. Private empirical risk minimization: Efficient algorithms and tight error bounds. In 2014 IEEE 55th Annual Symposium on Foundations of Computer Science (FOCS), pages 464--473, 2014. URL http://dx.doi.org/10.1109/FOCS.2014.56. Google ScholarDigital Library
- John C. Duchi, Michael I. Jordan, and Martin J. Wainwright. Local privacy and statistical minimax rates. In 2013 IEEE 54th Annual Symposium on Foundations of Computer Science (FOCS), pages 429--438, 2013. URL http://dx.doi.org/10.1109/FOCS.2013.53. Google ScholarDigital Library
- Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate. Stochastic gradient descent with differentially private updates. In Proceedings of the 2013 Global Conference on Signal and Information Processing (GlobalSIP 2013), pages 245--248, 2013. URL http://dx.doi.org/10.1109/GlobalSIP.2013.6736861.Google ScholarCross Ref
- Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate. Learning from data with heterogeneous noise using sgd. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, pages 894--902, 2015. URL http://jmlr.org/proceedings/papers/v38/song15.html.Google Scholar
- Stanley L. Warner. Randomized response: a survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60 (309): 63--69, 1965. URL http://dx.doi.org/10.1080/01621459.1965.10480775.Google ScholarCross Ref
- Frank McSherry and Kunal Talwar. Mechanism design via differential privacy. In 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS), pages 94--103, October 2007. URL http://dx.doi.org/10.1109/FOCS.2007.41. Google ScholarDigital Library
- Simon Tong and Daphne Koller. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2: 45--66, November 2001. URL http://www.jmlr.org/papers/v2/tong01a.html. Google ScholarDigital Library
- Victoria J. Hodge and Jim Austin. A survey of outlier detection methodologies. Artificial Intelligence Review, 22 (2): 85--126, 2004. URL http://dx.doi.org/10.1007/s10462-004--4304-y. Google ScholarDigital Library
- Saharon Rosset and Aron Inger. KDD-cup 99: knowledge discovery in a charitable organization's donor database. ACM SIGKDD Explorations Newsletter, 1 (2): 85--90, 2000. 10.1145/846183.846204. URL http://doi.acm.org/10.1145/846183.846204. Google ScholarDigital Library
- Hua Tang and Zhuolin Cao. Machine learning-based intrusion detection algorithms. Journal of Computational Information Systems, 5 (6): 1825--1831, 2009.Google Scholar
- Martin Zinkevich. Online convex programming and generalized infinitesimal gradient ascent. In Proceedings of the Twentieth International Conference on Machine Learning (ICML), pages 928--936, 2003. URL http://www.aaai.org/Papers/ICML/2003/ICML03-120.pdf.Google Scholar
- Shai Shalev-Shwartz. Online learning and online convex optimization. Foundations and Trends in Machine Learning, 4 (2): 107--194, 2011. URL http://dx.doi.org/10.1561/2200000018. Google ScholarDigital Library
- H. Brendan McMahan. A survey of algorithms and analysis for adaptive online learning. arXiv preprint arXiv:1403.3465, 2014. URL http://arxiv.org/abs/1403.3465.Google Scholar
- Burr Settles. Active learning literature survey. Computer Sciences Technical Report 1648. 2010. URL http://burrsettles.com/pub/settles.activelearning.pdf.Google Scholar
- Maria-Florina Balcan, Alina Beygelzimer, and John Langford. Agnostic active learning. In Proceedings of the 23rd international conference on Machine learning, pages 65--72, 2006. URL http://doi.acm.org/10.1145/1143844.1143853. Google ScholarDigital Library
- Yifan Fu, Xingquan Zhu, and Bin Li. A survey on instance selection for active learning. Knowledge and Information Systems, 35 (2): 249--283, 2013. URL http://dx.doi.org/10.1007/s10115-012-0507--8.Google ScholarCross Ref
- Steve Hanneke. Rates of convergence in active learning. The Annals of Statistics, 39 (1): 333--361, 2011. URL http://dx.doi.org/10.1214/10-AOS843.Google ScholarCross Ref
- Sivan Sabato and Tom Hess. Interactive algorithms: from pool to stream. In Proceedings of the 2016 Conference On Learning Theory (COLT 2016), pages 1419--1439, 2016. URL http://www.jmlr.org/proceedings/papers/v49/sabato16.Google Scholar
- Rui M. Castro and Robert D. Nowak. Minimax bounds for active learning. IEEE Transactions on Information Theory, 54 (5): 2339--2353, 2008. URL http://dx.doi.org/10.1109/TIT.2008.920189. Google ScholarDigital Library
- Michael Horstein. Sequential transmission using noiseless feedback. IEEE Transactions on Information Theory, 9 (3): 136--143, 1963. URL http://dx.doi.org/10.1109/TIT.1963.1057832. Google ScholarDigital Library
- Cynthia Dwork and Aaron Roth. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9 (3--4): 211--407, 2014. URL http://dx.doi.org/10.1561/0400000042. Google ScholarDigital Library
- John C. Duchi, Michael I. Jordan, and Martin J. Wainwright. Privacy aware learning. Journal of the ACM (JACM), 61 (6): 38:1--38:57, 2014. URL http://doi.acm.org/10.1145/2666468. Google ScholarDigital Library
- Dwork, Naor, Pitassi, and Rothblum}dwork2010continuousCynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N. Rothblum. Differential privacy under continual observation. In Proceedings of the forty-second ACM symposium on Theory of computing, pages 715--724, 2010. URL http://doi.acm.org/10.1145/1806689.1806787. Google ScholarDigital Library
- Dwork, Naor, Pitassi, Rothblum, and Yekhanin}dwork2010panCynthia Dwork, Moni Naor, Toniann Pitassi, Guy N. Rothblum, and Sergey Yekhanin. Pan-private streaming algorithms. In Proceedings of The First Symposium on Innovations in Computer Science (ICS 2010), 2010. URL http://www.wisdom.weizmann.ac.il/mathusers/naor/PAPERS/pan_private.pdf.Google Scholar
- Prateek Jain, Pravesh Kothari, and Abhradeep Thakurta. Differentially private online learning. In Proceedings of the 25th Annual Conference on Learning Theory (COLT 2012), volume 23 of JMLR Workshop and Conference Proceedings, pages 24.1--24.34, 2012. URL http://www.jmlr.org/proceedings/papers/v23/jain12/jain12.Google Scholar
- Maria-Florina Balcan and Vitaly Feldman. Statistical active learning algorithms. In Advances in Neural Information Processing Systems, pages 1295--1303, 2013. URL http://papers.nips.cc/paper/5101-statistical-active-learning-algorithms.pdf. Google ScholarDigital Library
- Larry Wasserman and Shuheng Zhou. A statistical framework for differential privacy. Journal of the American Statistical Association, 105 (489): 375--389, 2010. URL http://dx.doi.org/10.1198/jasa.2009.tm08651.Google ScholarCross Ref
- Peter Kairouz, Sewoong Oh, and Pramod Viswanath. The composition theorem for differential privacy. In Proceedings of The 32nd International Conference on Machine Learning, pages 1376--1385, 2015. URL http://www.jmlr.org/proceedings/papers/v37/kairouz15.Google ScholarDigital Library
- Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate. Differentially private empirical risk minimization. Journal of Machine Learning Research, 12: 1069--1109, 2011. URL http://www.jmlr.org/papers/v12/chaudhuri11a. Google ScholarDigital Library
- Frank McSherry. Privacy integrated queries: an extensible platform for privacy-preserving data analysis. Communications of the ACM, 53 (9): 89--97, 2010. URL http://doi.acm.org/10.1145/1810891.1810916. Google ScholarDigital Library
- Yann Lecun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86 (11): 2278--2324, 1998. URL http://dx.doi.org/10.1109/5.726791.Google ScholarCross Ref
Index Terms
- Differentially Private Online Active Learning with Applications to Anomaly Detection
Recommendations
Differentially Private Noisy Search with Applications to Anomaly Detection (Abstract)
AISec '17: Proceedings of the 10th ACM Workshop on Artificial Intelligence and SecurityWe consider the problem of privacy-sensitive anomaly detection - screening to detect individuals, behaviors, areas, or data samples of high interest. What defines an anomaly is context-specific; for example, a spoofed rather than genuine user attempting ...
A novel anomaly detection approach based on ensemble semi-supervised active learning (ADESSA)
Highlights- ADESSA detects attacks in CPS when traffic is rare labeled, unbalanced and unknown attacks exist.
- ADESSA builds a balanced training set including high-information and low-information samples with limited budget.
- Adding low-...
AbstractAs an industrial infrastructure, the safety and reliability of the Cyber-Physical System requires the effective anomaly detection. However, the existing detection methods have bottleneck in the face of insufficient training datasets. This work ...
Practical differentially private online advertising
AbstractPowered by machine learning technology, online advertising achieves accurate advertisement delivery to potential customers according to online user profiles. However, it raises serious privacy concerns since the learning process may ...
Comments