Search Results for author:
Found 14 papers, 3 papers with code
Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems
Global financial crime activity is driving demand for machine learning solutions in fraud prevention.
CDBB West Cambridge Digital Twin: Lessons Learned
The report describes the digital architecture developed for the West Cambridge Digital Twin, particularly focussed on real-time sensor data collection and analysis with a privacy framework allowing occupants of the buildings to be first-class participants in the system.
Distributed data analytics
A contrasting approach, distributed data analytics, where code and models for training and inference are distributed to the places where data is collected, has been boosted by two recent, ongoing developments: increased processing power and memory capacity available in user devices at the edge of the network, such as smartphones and home assistants; and increased sensitivity to the highly intrusive nature of many of these devices and services and the attendant demands for improved privacy.
Revisiting IoT Device Identification
Internet-of-Things (IoT) devices are known to be the source of many security problems, and as such, they would greatly benefit from automated management.
Data Management for Building Information Modelling in a Real-Time Adaptive City Platform
Legacy Building Information Modelling (BIM) systems are not designed to process the high-volume, high-velocity data emitted by in-building Internet-of-Things (IoT) sensors.
Human-Computer Interaction
The Case for Retraining of ML Models for IoT Device Identification at the Edge
We therefore evaluate our approach using hardware resources and data sources representative of those that would be available at the edge of the network, such as in an IoT deployment.
Fast Flexible Paxos: Relaxing Quorum Intersection for Fast Paxos
Fast Paxos bypasses the leader but has stronger quorum intersection requirements.
Distributed, Parallel, and Cluster Computing
Paxos vs Raft: Have we reached consensus on distributed consensus?
Raft's approach is surprisingly efficient given its simplicity as, unlike Paxos, it does not require log entries to be exchanged during leader election.
Distributed, Parallel, and Cluster Computing
Network Service Dependencies in Commodity Internet-of-Things Devices
We continue to see increasingly widespread deployment of IoT devices, with apparent intent to embed them in our built environment likely to accelerate if smart city and related programmes succeed.
Networking and Internet Architecture
A Generalised Solution to Distributed Consensus
Distributed consensus, the ability to reach agreement in the face of failures and asynchrony, is a fundamental primitive for constructing reliable distributed systems from unreliable components.
Distributed, Parallel, and Cluster Computing
User-centric Composable Services: A New Generation of Personal Data Analytics
Machine Learning (ML) techniques, such as Neural Network, are widely used in today's applications.
Probabilistic Synchronous Parallel
Most machine learning and deep neural network algorithms rely on certain iterative algorithms to optimise their utility/cost functions, e. g. Stochastic Gradient Descent.
Privacy-Preserving Personal Model Training
Many current Internet services rely on inferences from models trained on user data.
Personal Data: Thinking Inside the Box
We propose there is a need for a technical platform enabling people to engage with the collection, management and consumption of personal data; and that this platform should itself be personal, under the direct control of the individual whose data it holds.
Computers and Society
