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Found 5 papers, 1 papers with code
Verifiable Fairness: Privacy-preserving Computation of Fairness for Machine Learning Systems
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.
A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards
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.
Anti-Counterfeiting for Polymer Banknotes Based on Polymer Substrate Fingerprinting
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
Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context
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.
The relationship between trust in AI and trustworthy machine learning technologies
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.
