Related papers: A Java Data Security Framework (JDSF) and its Case…
Federated learning (FL) is inherently susceptible to privacy breaches and poisoning attacks. To tackle these challenges, researchers have separately devised secure aggregation mechanisms to protect data privacy and robust aggregation…
Java is the "go-to" programming language choice for developing scalable enterprise cloud applications. In such systems, even a few percent CPU time savings can offer a significant competitive advantage and cost saving. Although performance…
Today's mobile devices sense, collect, and store huge amounts of personal information, which users share with family and friends through a wide range of applications. Once users give applications access to their data, they must implicitly…
The growing development of artificial intelligence based solutions, together with privacy legislation, has driven the rise of the so-called privacy preserving machine learning architectures, such as federated learning. While federated…
Many password alternatives for web authentication proposed over the years, despite having different designs and objectives, all predominantly rely on the knowledge of some secret. This motivates us, herein, to provide the first detailed…
Spring security is tremendously popular among practitioners for its ease of use to secure enterprise applications. In this paper, we study the application framework misconfiguration vulnerabilities in the light of Spring security, which is…
Security and privacy are the inherent problems in RFID communications. There are several protocols have been proposed to overcome those problems. Hash chain is commonly employed by the protocols to improve security and privacy for RFID…
With the advent of machine learning in applications of critical infrastructure such as healthcare and energy, privacy is a growing concern in the minds of stakeholders. It is pivotal to ensure that neither the model nor the data can be used…
In a Public Safety (PS) situation, agents may require critical and personally identifiable information. Therefore, not only does context and location-aware information need to be available, but also the privacy of such information should be…
Software obfuscation or obscuring a software is an approach to defeat the practice of reverse engineering a software for using its functionality illegally in the development of another software. Java applications are more amenable to…
The continuous strengthening of the security posture of IoT ecosystems is vital due to the increasing number of interconnected devices and the volume of sensitive data shared. The utilisation of Machine Learning (ML) capabilities in the…
The Smart home possesses the capability of facilitating home services to their users with the systematic advance in The Internet of Things (IoT) and information and communication technologies (ICT) in recent decades. The home service…
Software design patterns are standard solutions to common problems in software design and architecture. Knowing that a particular module implements a design pattern is a shortcut to design comprehension. Manually detecting design patterns…
Biometric authentication systems pose privacy risks, as leaked templates such as iris or fingerprints can lead to security breaches. Fully Homomorphic Encryption (FHE) enables secure encrypted evaluation, but its deployment is hindered by…
Software privacy provides the ability to limit data access to unauthorized parties. Privacy is achieved through different means, such as implementing GDPR into software applications. However, previous research revealed that the lack of poor…
Pushed by market forces, software development has become fast-paced. As a consequence, modern development projects are assembled from 3rd-party components. Security & privacy assurance techniques once designed for large, controlled updates…
Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model. The assumption of a trusted server who has access to user information is ill-suited in many…
We initiate the study of distribution testing under \emph{user-level} local differential privacy, where each of $n$ users contributes $m$ samples from the unknown underlying distribution. This setting, albeit very natural, is significantly…
Lack of usability of security Application Programming In- terfaces (APIs) is one of the main reasons for mistakes that programmers make that result in security vulnerabilities in software applications they develop. Especially, APIs that…
With an increasing focus on data privacy, there have been pilot studies on recommender systems in a federated learning (FL) framework, where multiple parties collaboratively train a model without sharing their data. Most of these studies…