Related papers: Certifying Provenance of Scientific Datasets with …
Credential fraud is a widespread practice that undermines investment and confidence in higher education systems and bears significant economic and social costs. Legacy credential verification systems are typically time-consuming, costly,…
The interconnectedness of people, services and devices is a defining aspect of the digital revolution, and, secure digital identities are an important prerequisite for secure and legally compliant information exchange. Existing approaches…
As machine- and AI-generated content proliferates, protecting the intellectual property of generative models has become imperative, yet verifying data ownership poses formidable challenges, particularly in cases of unauthorized reuse of…
Code agents and empirical software engineering rely on public code datasets, yet these datasets lack verifiable quality guarantees. Static 'dataset cards' inform, but they are neither auditable nor do they offer statistical guarantees,…
Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have emerged to address these issues. This paradigm relies on generative AI models to generate unbiased, privacy-preserving data while maintaining…
Sharing data can often enable compelling applications and analytics. However, more often than not, valuable datasets contain information of a sensitive nature, and thus, sharing them can endanger the privacy of users and organizations. A…
Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic because of their dependence on the background knowledge of…
Every digital process needs to consume some data in order to work properly. It is very common for applications to use some external data in their processes, getting them by sources such as external APIs. Therefore, trusting the received…
Successful data-driven science requires complex data engineering pipelines to clean, transform, and alter data in preparation for machine learning, and robust results can only be achieved when each step in the pipeline can be justified, and…
Ensuring safe adoption of AI tools in healthcare hinges on access to sufficient data for training, testing and validation. In response to privacy concerns and regulatory requirements, using synthetic data has been suggested. Synthetic data…
The spread of false and misleading information is receiving significant attention from legislative and regulatory bodies. Consumers place trust in specific sources of information, so a scalable, interoperable method for determining the…
With the aim of informing sound policy about data sharing and privacy, we describe successful re-identification of patients in an Australian de-identified open health dataset. As in prior studies of similar datasets, a few mundane facts…
The use of blockchains for data certification and traceability is now well established in both the literature and practical applications. However, while blockchain-based certification of individual data is clear and straightforward, the use…
Self Sovereign Identity (SSI) is an emerging identity system that facilitates secure credential issuance and verification without placing trust in any centralised authority. To bypass central trust, most SSI implementations place blockchain…
Authentication plays a significant part in dealing with security in public and private sectors such as healthcare systems, banking system, transportation system and law and security. Biometric technology has grown quickly recently,…
Introduction: The amount of data generated by original research is growing exponentially. Publicly releasing them is recommended to comply with the Open Science principles. However, data collected from human participants cannot be released…
Users increasingly rely on identity providers for accessing online services and resources. However, centralized identity systems often compromise user privacy due to online activity tracking or data breaches. At the same time, many online…
Scientific fact-checking aims to determine the veracity of scientific claims by retrieving and analysing evidence from research literature. The problem is inherently more complex than general fact-checking since it must accommodate the…
Data stewards seeking to provide access to large-scale social science data face a difficult challenge. They have to share data in ways that protect privacy and confidentiality, are informative for many analyses and purposes, and are…
The popularity of the Internet of Things (IoT) has driven its usage in our homes and industries over the past 10-12 years. However, there have been some major issues related to identity management and ownership transfer involving IoT…