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Topic
Artificial intelligence Collaboration Community Safety Countering child exploitation Countering misinformation Data management Data science Digital forensics Ethical use of data Explainable AI Image classification Infrastructure Interdisciplinary research Law Enforcement Machine learning Masters project Perceptual hashing Student project Clear
Category
Countering online child exploitation Curating ethical datasets Detecting illegal firearms Interdisciplinary AI research Managing sensitive data Uncategorized Clear
Type
Academic publications Briefing Note Report Tech note Video Clear
2 results
Report
TR22/03: The Data Airlock: infrastructure for restricted data informatics

Access to operational data from outside an organisation may be prohibited for a variety of reasons. There are significant challenges when performing collaborative data science work against such restricted data.

This report describes a range of causes and risks associated with restricted data along with the social, environmental, data, and cryptographic measures that may be used to mitigate such issues. These are generally inadequate for restricted data contexts. We introduce the ’Data Airlock’, secure infrastructure that facilitates eyes-off data-science workloads. After describing our use-case, we detail the architecture and implementation of a first, single-organisation version of this infrastructure. We conclude with learnings from this implementation, and outline requirements for a second, federated version.

2022
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Tech note
TN22/03: Law Enforcement Data Interoperability (Student thesis paper)

In law enforcement (LE), interoperability, i.e., the ability to exchange information between databases and systems, enhances the ability of agencies to detect and investigate crime. A fundamental way of improving interoperability is data integration, but integrating LE databases is often difficult due to heterogeneity of database types and the semantics of the data. In this study, an ontology-based and Linked Data approach for integrating heterogeneous LE databases is proposed.

The approach is evaluated for use in an operational setting by LE data domain experts. The evaluation feedback indicates that the approach has the potential to address some of the common challenges faced when integrating heterogeneous LE databases, and could provide benefit if used in an LE agency’s operational systems.

2022
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