Related papers: Guaranteeing Anonymity in Attribute-Based Authoriz…
Decentralized systems can be more resistant to operator mischief than centralized ones, but they are substantially harder to develop, deploy, and maintain. This cost is dramatically reduced if the decentralized part of the system can be…
Security requirements in distributed software systems are inherently dynamic. In the case of authorization policies, resources are meant to be accessed only by authorized parties, but the authorization to access a resource may be…
Blockchain-based Attribute-Based Access Control (BC-ABAC) offers a decentralized paradigm for secure data governance but faces two inherent challenges: the transparency of blockchain ledgers threatens user privacy by enabling…
The current trajectory privacy protection technology only considers the temporal and spatial attributes of trajectory data, but ignores the social attributes. However, there is an intrinsic relationship between social attributes and human…
This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal role in the overall functioning of Autonomous Vehicles (AVs).…
A tremendous amount of individual-level data is generated each day, of use to marketing, decision makers, and machine learning applications. This data often contain private and sensitive information about individuals, which can be disclosed…
The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algorithms can have on different groups. Examples include credit…
A team consisting of an unknown number of mobile agents, starting from different nodes of an unknown network, possibly at different times, have to meet at the same node. Agents are anonymous (identical), execute the same deterministic…
In the scenario of the Voice Privacy challenge, anonymization is achieved by converting all utterances from a source speaker to match the same target identity; this identity being randomly selected. In this context, an attacker with maximum…
A secure human identification protocol aims at authenticating human users to a remote server when even the users' inputs are not hidden from an adversary. Recently, the authors proposed a human identification protocol in the RSA Conference…
Distributed machine learning has been widely studied in order to handle exploding amount of data. In this paper, we study an important yet less visited distributed learning problem where features are inherently distributed or vertically…
With increasing emphasis on transparency in digital governance, users expect more than silence when their access requests are denied by a system. However, authorization methods are notorious for their inability to provide any form of…
Recently, productization of face recognition and identification algorithms have become the most controversial topic about ethical AI. As new policies around digital identities are formed, we introduce three face access models in a…
The operationalization of algorithmic fairness comes with several practical challenges, not the least of which is the availability or reliability of protected attributes in datasets. In real-world contexts, practical and legal impediments…
Large Language Models (LLMs) have demonstrated advanced capabilities in both text generation and comprehension, and their application to data archives might facilitate the privatization of sensitive information about the data subjects. In…
Digital identity seems like a prerequisite for digital democracy: how can we ensure "one person, one vote" online without identifying voters? But digital identity solutions - ID checking, biometrics, self-sovereign identity, and trust…
Ensuring privacy of sensitive data is essential in many contexts, such as healthcare data, banks, e-commerce, wireless sensor networks, and social networks. It is common that different entities coordinate or want to rely on a third party to…
Preserving the individuals' privacy in sharing spatial-temporal datasets is critical to prevent re-identification attacks based on unique trajectories. Existing privacy techniques tend to propose ideal privacy-utility tradeoffs, however,…
Fairness holds a pivotal role in the realm of machine learning, particularly when it comes to addressing groups categorised by protected attributes, e.g., gender, race. Prevailing algorithms in fair learning predominantly hinge on…
While personalized recommendations systems have become increasingly popular, ensuring user data protection remains a top concern in the development of these learning systems. A common approach to enhancing privacy involves training models…