Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...
Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the stat ...
Background: Increasingly, hospitals and research institutes are developing technical solutions for sharing patient data in a privacy preserving manner. Two of these technical solutions are homomorphic encryption and distributed ledger technology. Homomorph ...
Hardware security concerns such as IP piracy and hardware Trojans have triggered research into circuit protection and malicious logic detection from various design perspectives. In this paper, emerging technologies are investigated by leveraging their uniq ...
Data security in a mobile context is a critical issue. Over the last few years a new category of location-based services, the Enterprise LBS (ELBS), has emerged focusing on the demands of mobility in organisations. These applications pose challenging requi ...
In this work, MOS Current Mode Logic (MCML) is analyzed for low power, low noise, mixed signal applications demanding high security such as embedded cryptographic processors and smart cards. We emphasize the possible extension of MCML gate usage for low sp ...
