The Secure Machine Learning (SecML) Project @ Melbourne

Statistical Machine Learning – born out of AI, Data Mining, Machine Learning and Statistics – has enabled numerous products and underpins industry trends in Big Data, Data Science and Analytics. Classic SML tasks include among others classification, regression, density estimation, and clustering, where traditional performance measures are statistical (accuracy) and computational (runtime/storage). Increasingly, however, SML is being deployed in systems in which malicious behaviour is incentivised. In such cases, data assumptions such as stationarity, independence, or even stochasticity made by most SML techniques mismatches the reality of data manipulation or misuse. In Adversarial Machine Learning, the attacker may wish to breach the integrity, availability or privacy of an SML system, motivating our interest in evaluating the security/privacy of existing SML approaches and designing improvements. Our work employs a wide range of techniques from kernel methods, Bayesian statistics, online learning theory (learning-theoretic game theory, involving repeated games), robust statistics, differential privacy, with applications in cloud computing, open-source software, network security, risk management, and data privacy. We are keen to make fundamental contributions and engage with government and industry.


Benjamin Rubinstein (Senior Lecturer, Project Lead)
Samuel Jenkins (Masters student)
Zuhe Zhang (PhD student)

Collaborators Past & Present

Drs. Tansu Alpcan, Jeffrey Chan, Prof. Christopher Leckie, A/Prof. Sanming Zhou (University of Melbourne, Australia)
Dr. Adam Barth (Google)
Prof. Peter Bartlett (UC Berkeley USA & QUT Australia), Profs. Anthony Joseph, Dawn Song, J. Doug Tygar (UC Berkeley, USA)
Dr. Battista Biggio, Prof. Fabio Roli (University of Cagliari, Italy)
Dr. Christos Dimitrakakis, Prof. Aikaterini Mitrokotsa (Chalmers University of Technology, Sweden)
Dr. Ling Huang (Intel Labs Berkeley, USA)
Prof. Arvind Narayanan (Princeton University, USA)
Dr. Blaine Nelson (University of Potsdam, Germany)
Prof. Elaine Shi (University of Maryland)
Dr. Nina Taft (Technicolor Palo Alto, USA)