Related papers: A User-Centric, Privacy-Preserving, and Verifiable…
Aiming to support a cross-sector and cross-border eGovernance paradigm for sharing common public services, this paper introduces an AI-enhanced solution that enables beneficiaries to participate in a decenntralized network for effective big…
Digital identities are increasingly important for mediating not only digital but also physical service transactions. Managing such identities through centralized providers can cause both availability and privacy concerns: single points of…
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…
Privacy preservation is a big concern for various sectors. To protect individual user data, one emerging technology is differential privacy. However, it still has limitations for datasets with frequent queries, such as the fast accumulation…
Objective: To enable privacy-preserving learning of high quality generative and discriminative machine learning models from distributed electronic health records. Methods and Results: We describe general and scalable strategy to build…
Massive amounts of multimedia data (i.e., text, audio, video, graphics and animation) are being generated everyday. Conventionally, multimedia data are managed by the platforms maintained by multimedia service providers, which are generally…
The problem we address is the following: how can a user employ a predictive model that is held by a third party, without compromising private information. For example, a hospital may wish to use a cloud service to predict the readmission…
As Artificial Intelligence (AI) becomes more prevalent, protecting personal privacy is a critical ethical issue that must be addressed. This article explores the need for ethical AI systems that safeguard individual privacy while complying…
The scarcity of accessible, compliant, and ethically sourced data presents a considerable challenge to the adoption of artificial intelligence (AI) in sensitive fields like healthcare, finance, and biomedical research. Furthermore, access…
With the increasing breaches and security threats that endanger health data, ensuring patients' privacy is essential. To that end, the research community has proposed various privacy-preserving approaches based on cryptography, hashing, or…
INTRODUCTION: The proliferation of the amalgamation of IoT and edge computing has increased the demand for decentralised trust and security mechanisms capable of operating across heterogeneous and resource-limited devices. Approaches such…
The sharing of public key information is central to the digital credential security model, but the existing Web PKI with its opaque Certification Authorities and synthetic attestations serves a very different purpose. We propose a new…
For AI technology to fulfill its full promises, we must have effective means to ensure Responsible AI behavior and curtail potential irresponsible use, e.g., in areas of privacy protection, human autonomy, robustness, and prevention of…
In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate…
In the expanding field of machine learning, federated learning has emerged as a pivotal methodology for distributed data environments, ensuring privacy while leveraging decentralized data sources. However, the heterogeneity of client data…
Data protection algorithms are becoming increasingly important to support modern business needs for facilitating data sharing and data monetization. Anonymization is an important step before data sharing. Several organizations leverage on…
The predominant centralized paradigm in educational data management currently suffers from several critical issues such as vulnerability to malicious tampering, a high prevalence of diploma counterfeiting, and the onerous cost of…
The need for a privacy management layer in today's systems started to manifest with the emergence of new systems for privacy-preserving analytics and privacy compliance. As a result, many independent efforts have emerged that try to provide…
Distributed energy resources (DERs) are gaining prominence due to their advantages in improving energy efficiency, reducing carbon emissions, and enhancing grid resilience. Despite the increasing deployment, the potential of DERs has yet to…
Privacy protection in digital databases does not demand that data should not be collected, stored or used, but that there should be guarantees that the data can only be used for pre-approved and legitimate purposes. We argue that a data…