I joined School of Computing and Information Systems as a senior lecturer in early 2020. Prior to joining the University of Melbourne I spent 6 years at Microsoft Research in Cambridge, UK, where I was a principal researcher leading work on Confidential AI and I/O side-channel mitigation. Between 2014 and 2016, I was the Microsoft Fellow at Darwin College in Cambridge University.
My research interests include privacy, integrity and security of machine learning algorithms, data analysis tools, multi-party computation and systems relying on cloud storage, computation and hardware. The goal is to identify and prevent potential vulnerabilities and to design and build efficient solutions with provable guarantees. I also enjoy working on algorithms, data structures and theory.
If you are interested in pursuing a Ph.D. or a Master's project in any of the areas listed above, please contact me directly.
Contact: oohrimenko @ unimelb dоt edu dоt au
- Received Facebook research award on the "Role of applied cryptography in a privacy-focused ecosystem".
- Received Facebook "Probability and Programming research award" with Ben Rubinstein and Toby Murray.
- Our paper on Analyzing Information Leakage of Updates to Natural Language Models with Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Rühle, Andrew Paverd, Boris Köpf, and Marc Brockschmidt to appear at CCS 2020.
Areas I have worked on include:
- multi-party machine learning: confidentiality, privacy, data contamination, incentives
- data-oblivious algorithms for memory access, shuffling and sampling data, MapReduce, machine learning and graph drawing
- differential privacy
- cache and memory side-channels in secure hardware (e.g., Intel SGX)
- verifiable computation and authenticated data structures (with zero knowledge)
- searchable encryption
- blockchain framework and applications
- constraint programming and optimization
(Full list here)
- Oblivious Sampling Algorithms for Private Data Analysis by S. Sasy and O. Ohrimenko. In NeurIPS, 2019.
- An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors by J. Allen, B. Ding, J. Kulkarni, H. Nori, O. Ohrimenko, and S. Yekhanin. In NeurIPS, 2019.
- Contamination Attacks and Mitigation in Multi-Party Machine Learning by J. Hayes and O. Ohrimenko. In NeurIPS, 2018.
- Structured Encryption and Leakage Suppression by S. Kamara, T. Moataz, and O. Ohrimenko. In CRYPTO, 2018.
At Microsoft Research I was part of Confidential Computing group when our group contributed to:
- Azure Confidential Computing
- Projects highlighted by CTO of Microsoft Azure: oblivious computation, multi-party ML, and confidential inference (1:06:08).
- Confidential Consortium Framework
I was also lucky to work with many bright interns:
- Dongge Han (Oxford University), 2019
- Wanrong Zhang (Georgia Institute of Technology), 2019
- Sajin Sasy (University of Waterloo), 2019
- Jamie Hayes (University College London), 2018
- Kartik Nayak (The University Of Maryland), 2017
- Lawrence Esswood (Cambridge University), 2016
- Daniel Gruss (Graz University of Technology), 2016
- Sameer Wagh (Princeton University), 2016
- Esha Ghosh (Brown University), 2015
- Aastha Mehta (MPI-SWS), 2014
- Divya Sharma (Carnegie Mellon University), 2014
Contributed to the report on Privacy Enhancing Technologies by the Royal Society.
(Some) Invited talks
- Secure hardware for privacy-enhanced computation, UK Royal Society, 2019
- Update on Confidential Computing, RSA, 2019
- Confidential Machine Learning on Trusted Processors, Data, Learning and Inference (DALI), 2018
- Sailing across the Side channel towards Confidential Computing, Real World Crypto, 2018
- Confidential Machine Learning, AI Summer School, 2017
- CRYPTO 2020
- Computer Security Foundations Symposium (CSF 2020)
- Real World Crypto (RWC 2020)
- ICML workshop on Privacy (PiMLAI 2018)
- Conference on Computer and Communications Security (CCS 2018, CCS 2019)
- Financial Cryptography and Data Security (FC 2018, FC 2019)
- Conference on Distributed Computing Systems (ICDCS 2016)
- CCS Workshop on Privacy in the Electronic Society (WPES 2014, WPES 2015)
Reviewer for ICML 2020, AAAI 2020, NeurIPS 2019, ICML 2019, NeurIPS 2018.
- 2012 IBM Research Zurich with Christian Cachin
- 2012 MSR Redmond with Seny Kamara
- 2010 Google NYC, Search team with Jeff Cox
- 2009 Google CA, AdSense
- 2008 Optiver, Australia
- 17 for ’17: Microsoft researchers on what to expect in 2017 and 2027
- Postdoc attendee of Heidelberg Laureate Forum
- On judge panel of Codess Hackathon
- My Erdős number is 3 (times 3 from Claire Mathieu, Michael Mitzenmacher and Eli Upfal)
- Olya vs. Olga: Both are fine with a slight preference for the former