<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/40491F0C-3E8A-47A2-A012-3F7CD2F54700" ns1:id="40491F0C-3E8A-47A2-A012-3F7CD2F54700"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/1A0E605C-ED90-46B5-BDB9-57F913FD3F0D" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/3EB71F0B-5407-4D64-9160-21B51F6CED61" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/3EB71F0B-5407-4D64-9160-21B51F6CED61" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/65701362-BAA1-4F61-8BBC-E0EF3D08172D" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2019-12-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/3A9900B6-1F6F-4A51-A8BF-E81C8D15C87B" ns1:rel="FUND" ns1:start="2018-07-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">133514</ns2:identifier></ns2:identifiers><ns2:title>Pimbox: Personalised private image management, using innovative neural networks</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Feasibility Studies</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>PIMBOX is an Experimental Development project in the field of Machine Learning (&amp;quot;ML&amp;quot;). ML is an extremely active area of academic research whose exploitation to date is almost exclusively restricted to large-scale implementations such as data mining in large corporate environments and the provision of global, on-line services. Recent advances in research have shown that it is possible to reduce the scale of the computational resources required to run suitably-designed and appropriately-trained ML systems. (The importance of large and appropriate training sets is undiminished.) This holds out the possibility of bringing many of the capabilities of large-scale systems into low-cost, individualised implementations. The best-known examples are sensor signal processing for &amp;quot;self-driving&amp;quot; cars and the long-anticipated IOT. The PIMBOX project, on the other hand, challenges several of the pre-conceptions of ML-based on-line services. These require users to upload unencrypted private data to take advantage of the ML-based tools and services that are offered free-of-charge in return. &amp;quot;If it's free, you are the product.&amp;quot; PIMBOX aims to provide self-trainable ML data management and curation tools for secure local home and commercial networks. These will have better usability than the best on-line services. Users may then encrypt private and personal data before using on-line backup or sharing. The project engages the close involvement of leading academics in the field of ML, with highly-experienced industrial h/w and s/w engineers, and market and business developers.</ns2:abstractText></ns2:project>