28 research outputs found
A Dynamic Distributed Architecture for Preserving Privacy of Medical IoT Monitoring Measurements
Knowledge is at the Edge! How to Search in Distributed Machine Learning Models
With the advent of the Internet of Things and Industry 4.0 an enormous amount
of data is produced at the edge of the network. Due to a lack of computing
power, this data is currently send to the cloud where centralized machine
learning models are trained to derive higher level knowledge. With the recent
development of specialized machine learning hardware for mobile devices, a new
era of distributed learning is about to begin that raises a new research
question: How can we search in distributed machine learning models? Machine
learning at the edge of the network has many benefits, such as low-latency
inference and increased privacy. Such distributed machine learning models can
also learn personalized for a human user, a specific context, or application
scenario. As training data stays on the devices, control over possibly
sensitive data is preserved as it is not shared with a third party. This new
form of distributed learning leads to the partitioning of knowledge between
many devices which makes access difficult. In this paper we tackle the problem
of finding specific knowledge by forwarding a search request (query) to a
device that can answer it best. To that end, we use a entropy based quality
metric that takes the context of a query and the learning quality of a device
into account. We show that our forwarding strategy can achieve over 95%
accuracy in a urban mobility scenario where we use data from 30 000 people
commuting in the city of Trento, Italy.Comment: Published in CoopIS 201
Data provenance to audit compliance with privacy policy in the Internet of Things
Managing privacy in the IoT presents a significant challenge. We make the case that information obtained by auditing the flows of data can assist in demonstrating that the systems handling personal data satisfy regulatory and user requirements. Thus, components handling personal data should be audited to demonstrate that their actions comply with all such policies and requirements. A valuable side-effect of this approach is that such an auditing process will highlight areas where technical enforcement has been incompletely or incorrectly specified. There is a clear role for technical assistance in aligning privacy policy enforcement mechanisms with data protection regulations. The
first step necessary in producing technology to accomplish this alignment is to gather evidence of data flows. We describe our work producing, representing and querying audit data and discuss outstanding challenges.Engineering and Applied Science
A Framework to Navigate the Privacy Trade-offs for Human-Centred Manufacturing
New technological advances can offer personalised and timely services for industry workers. Exoskeletons, HoloLens, Process Mining and Social Knowledge Networks are some of these services offered to workers by the EU HuMan project. These services could alleviate a worker’s physical stress, their cognitive load or provide help based on the knowledge and experiences of their peers. The successful application of several such services depends on the availability of data about a worker’s state, including their performance. This paper focusses on the design of cognitive systems that provide personalised services while respecting a worker’s privacy and the needs of an organisation. We present a framework for supporting privacy by design and the risks, threats and needs of users, organisations and developers.acceptedVersio