The extensive collection and processing of personal information in big data analytics has
given rise to serious privacy concerns, related to wide scale electronic surveillance, profiling …

How can one build a distributed framework that allows efficient deployment of a wide
spectrum of modern advanced machine learning (ML) programs for industrial-scale …

Machine-learning (ML) enables computers to learn how to recognise patterns, make
unintended decisions, or react to a dynamic environment. The effectiveness of trained …

Bitcoin is a digital currency that uses anonymous cryptographic identities to achieve
financial privacy. However, Bitcoin's promise of anonymity is broken as recent work shows …

Governments are under increasing pressure to publicly release collected data in order to
promote transparency, accountability, and innovation. Because much of the data they …

This paper considers the problem of secure data aggregation (mainly summation) in a
distributed setting, while ensuring differential privacy of the result. We study secure …

Data collection is required to be safe and efficient considering both data privacy and system
performance. In this paper, we study a new problem: distributed data sharing with privacy …

Privacy‐preserving analysis techniques in general and specifically privacy‐preserving
record linkage (PPRL) have become active fields of research in computer science, statistics …

In this paper, we will show that quantum private comparison protocol is secure when a
malicious third party is presented. The security of the protocol is considered in a cheat …

Intrusion detection systems (IDSs) have become a necessary component of computers and
information security framework. IDSs commonly deal with a large amount of data traffic and …