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Found 9 papers, 3 papers with code
Safe machine learning model release from Trusted Research Environments: The AI-SDC package
We present AI-SDC, an integrated suite of open source Python tools to facilitate Statistical Disclosure Control (SDC) of Machine Learning (ML) models trained on confidential data prior to public release.
ACRO: A multi-language toolkit for supporting Automated Checking of Research Outputs
This paper discusses the development of an open source tool ACRO, (Automatic Checking of Research Outputs) to assist researchers and data governance teams by distinguishing between: research output that is safe to publish; output that requires further analysis; and output that cannot be published because it creates substantial risk of disclosing private data.
Sentence encoding for Dialogue Act classification
In this study, we investigate the process of generating single-sentence representations for the purpose of Dialogue Act (DA) classification, including several aspects of text pre-processing and input representation which are often overlooked or underreported within the literature, for example, the number of words to keep in the vocabulary or input sequences.
Evolutionary n-level Hypergraph Partitioning with Adaptive Coarsening
This article presents a novel memetic algorithm which remains effective on larger initial hypergraphs.
Interactive Ant Colony Optimisation (iACO) for Early Lifecycle Software Design
Building on these findings, we propose a novel interactive ACO (iACO) approach to assist the designer in early lifecycle software design, in which the search is steered jointly by subjective designer evaluation as well as machine fitness functions relating the structural integrity and surrogate elegance of software designs.
Confidentiality and linked data
Data providers such as government statistical agencies perform a balancing act: maximising information published to inform decision-making and research, while simultaneously protecting privacy.
Protein Structured Reservoir computing for Spike-based Pattern Recognition
Nowadays we witness a miniaturisation trend in the semiconductor industry backed up by groundbreaking discoveries and designs in nanoscale characterisation and fabrication.
Machine Learning Models Disclosure from Trusted Research Environments (TRE), Challenges and Opportunities
In this paper we introduce the challenge of disclosing trained machine learning models from TREs.
GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)
This is a complex topic, and it is unreasonable to expect all TREs to be aware of all risks or that TRE researchers have addressed these risks in AI-specific training.
