no code implementations • 12 Sep 2023 • Ehsan Toreini, Maryam Mehrnezhad, Aad van Moorsel
In this paper, we propose Fairness as a Service (FaaS), a secure, verifiable and privacy-preserving protocol to computes and verify the fairness of any machine learning (ML) model.
1 code implementation • 2 Aug 2023 • Joshua Harrison, Ehsan Toreini, Maryam Mehrnezhad
With recent developments in deep learning, the ubiquity of micro-phones and the rise in online services via personal devices, acoustic side channel attacks present a greater threat to keyboards than ever.
no code implementations • 10 Mar 2021 • Shen Wang, Ehsan Toreini, Feng Hao
As compared with previous or existing anti-counterfeiting mechanisms for banknotes, our method has a distinctive advantage: it ensures that even in the extreme case when counterfeiters have procured the same printing equipment and ink as used by a legitimate government, counterfeiting banknotes remains infeasible because of the difficulty to replicate a stochastic manufacturing process.
Cryptography and Security
no code implementations • 17 Jul 2020 • Ehsan Toreini, Mhairi Aitken, Kovila P. L. Coopamootoo, Karen Elliott, Vladimiro Gonzalez Zelaya, Paolo Missier, Magdalene Ng, Aad van Moorsel
As a consequence, we survey in this paper the main technologies with respect to all four of the FEAS properties, for data-centric as well as model-centric stages of the machine learning system life cycle.
no code implementations • 27 Nov 2019 • Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, Aad van Moorsel
To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services.