Associate Professor Ben Rubinstein

  • Room: Level: 07 Room: 7.21
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Big Data (Scalable Learning, Data Integration)
  • Computer Security & Privacy (Cybersecurity, Data Privacy)
  • Statistical Machine Learning

Personal webpage


Dr. Rubinstein joined the University of Melbourne in 2013 as a R@MAP appointee, as a Senior Lecturer in Computing and Information Systems. Previously he gained four years of US industry experience in the research divisions of Microsoft, Google, Intel and Yahoo!; followed by a short stint at IBM Research Australia. As a full-time Researcher at Microsoft Research, Silicon Valley for close to 3 years, Rubinstein shipped production systems for entity resolution in Bing and the Xbox360 (driving huge success accounting for revenues in the $100m's); he actively researches topics in machine learning, security, privacy, and databases. Rubinstein earned the PhD in Computer Science from UC Berkeley under Peter Bartlett in 2010, collaborating closely with the SecML group, at the boundary of machine learning and security.

Recent publications

  1. Roohi, L.; Rubinstein, BIP.; Teague, V. Differentially-Private Two-Party Egocentric Betweenness Centrality. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. IEEE. 2019, Vol. 2019-April, pp. 2233-2241. DOI: 10.1109/INFOCOM.2019.8737405
  2. Li, Y.; Rubinstein, BIP.; Cohn, T. Truth inference at scale: A Bayesian model for adjudicating highly redundant crowd annotations. The World Wide Web Conference on - WWW '19. ACM. 2019, pp. 1028-1038. DOI: 10.1145/3308558.3313459
  3. Han, Y.; Rubinstein, B. Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks. . The AAAI Press. 2018.
  4. Fanaeepour, M.; Rubinstein, BIP. Differentially private counting of users' spatial regions. Knowledge and Information Systems. SPRINGER LONDON LTD. 2018, Vol. 54, Issue 1, pp. 5-32. DOI: 10.1007/s10115-017-1113-6
  5. Aye, ZMM.; Rubinstein, BIP.; Ramamohanarao, K. Fast manifold landmarking using locality-sensitive hashing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). SpringerLink. 2018, Vol. 10939 LNAI, pp. 452-464. DOI: 10.1007/978-3-319-93040-4_36
  6. Rubinstein, BIP.; Fanaeepour, M. Histogramming privately ever after: Differentially-private data-dependent error bound optimisation. 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE. 2018, pp. 1208-1211. DOI: 10.1109/ICDE.2018.00111
  7. Lyu, L.; Nandakumar, K.; Rubinstein, B.; Jin, J.; Bedo, J.; Palaniswami, M. PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid. IEEE Transactions on Industrial Informatics. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2018, Vol. 14, Issue 8, pp. 3733-3744. DOI: 10.1109/TII.2018.2803782
  8. Han, Y.; Rubinstein, BIP.; Abraham, T.; Alpcan, T.; De Vel, O.; Erfani, S.; Hubczenko, D.; Leckie, C.; Montague, P. Reinforcement learning for autonomous defence in software-defined networking. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2018, Vol. 11199 LNCS, pp. 145-165. DOI: 10.1007/978-3-030-01554-1_9
  9. Fish, B.; Reyzin, L.; Rubinstein, BIP. Sublinear-time adaptive data analysis. International Symposium on Artificial Intelligence and Mathematics, ISAIM 2018. 2018.
  10. Dimitrakakis, C.; Nelson, B.; Zhang, Z.; Mitrokotsa, A.; Rubinstein, BIP. Differential Privacy for Bayesian Inference through Posterior Sampling. Journal of Machine Learning Research. MICROTOME PUBL. 2017, Vol. 18.
  11. Liu, X.; Harwood, A.; Karunasekera, S.; Rubinstein, B.; Buyya, R. E-Storm: Replication-based State Management in Distributed Stream Processing Systems. 2017 46th International Conference on Parallel Processing (ICPP). IEEE COMPUTER SOC. 2017, pp. 571-580. DOI: 10.1109/ICPP.2017.66
  12. Verkade, H.; Lodge, JM.; Elliott, K.; Mulhern, TD.; Espinosa, AA.; Cropper, SJ.; Rubinstein, BIP. Exploring misconceptions as a trigger for enhancing student learning. Research and Development in Higher Education Series. Higher Education Research and Development Society of Australasia, Inc. 2017, Vol. 40, pp. 392-401.
  13. Marchant, NG.; Rubinstein, BIP. In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling. Proceedings of the VLDB Endowment. ASSOC COMPUTING MACHINERY. 2017, Vol. 10, Issue 11, pp. 1322-1333. DOI: 10.14778/3137628.3137642
  14. Lau, L.; Kankanige, Y.; Rubinstein, B.; Jones, R.; Christophi, C.; Muralidharan, V.; Bailey, J. Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation. Transplantation. LIPPINCOTT WILLIAMS & WILKINS. 2017, Vol. 101, Issue 4, pp. E125-E132. DOI: 10.1097/TP.0000000000001600
  15. Verkade, H.; Mulhern, TD.; Lodge, J.; Elliott, K.; Cropper, S.; Rubinstein, B.; Horton, A.; Elliott, C.; Espinosa, A.; Dooley, L.; Frankland, S.; Mulder, R.; Livett, M. Misconceptions as a trigger for enhancing student learning in higher education. . The University of Melbourne. 2017.
  16. Rubinstein, BIP.; Alda, F. Pain-free random differential privacy with sensitivity sampling. 34th International Conference on Machine Learning, ICML 2017. 2017, Vol. 6, pp. 4520-4529.
  17. Aldà, F.; Rubinstein, BIP. The bernstein mechanism: Function release under differential privacy. 31st AAAI Conference on Artificial Intelligence, AAAI 2017. Unknown. 2017, pp. 1705-1711.
  18. Han, Y.; Alpcan, T.; Chan, J.; Leckie, C.; Rubinstein, BIP. A Game Theoretical Approach to Defend Against Co-Resident Attacks in Cloud Computing: Preventing Co-Residence Using Semi-Supervised Learning. IEEE Transactions on Information Forensics and Security. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2016, Vol. 11, Issue 3, pp. 556-570. DOI: 10.1109/TIFS.2015.2505680
  19. Fanaeepour, M.; Rubinstein, B. Beyond Points and Paths: Counting Private Bodies. Proceedings - IEEE International Conference on Data Mining, ICDM. IEEE. 2016, pp. 131-140. DOI: 10.1109/ICDM.2016.0024
  20. Sanchez, I.; Aye, ZMM.; Rubinstein, BIP.; Ramamohanarao, K. Fast Trajectory Clustering using Hashing Methods. 2016 International Joint Conference on Neural Networks (IJCNN). IEEE. 2016, Vol. 2016-October, pp. 3689-3696. DOI: 10.1109/IJCNN.2016.7727674

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile