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Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...
To help fighting the COVID-19 pandemic, the Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT) project proposed a Decentralized Privacy-Preserving Proximity Tracing (DP3T) system. This helps tracking the spread of SARS-CoV-2 virus while keeping th ...
In Alan Westin's generally accepted definition of privacy, he describes it as an individual's right 'to control, edit, manage, and delete information about them[selves] and decide when, how, and to what extent information is communicated to others.' There ...
Website fingerprinting (WF) attacks can compromise a user’s online privacy, by learning network traffic patterns generated by websites through machine learning (ML) techniques. Such attacks remain unaffected by encryption and even defeat anonymity services ...
Although encryption hides the content of communications from third parties, metadata, i.e., the information attached to the content (such as the size or timing of communication) can be a rich source of details and context. In this dissertation, we demonstr ...
Disclosed herein is a method for labelling a portion of a device, said method comprising the steps of providing a first substrate layer of a transparent or translucent material, depositing a first coloured material onto said first substrate layer in a labe ...
In this thesis, we focus on the problem of achieving practical privacy guarantees in machine learning (ML), where the classic differential privacy (DP) fails to maintain a good trade-off between user privacy and data utility. Differential privacy guarantee ...
A composite multi-layer textile (1), comprising at least one nanofibre layer (11) with nanofibres with diameters below 100nm and one support layer (12) with microfibres with diameters below three microns, wherein the layers (11, 12) were produced by electr ...
The conceptual design phase is a fascinating moment to observe how a design task is interpreted, as the (often implicit) relative importance students accord to the various requirements and constraints offers a window into the thinking underpinning their de ...
The ever-growing number of edge devices (e.g., smartphones) and the exploding volume of sensitive data they produce, call for distributed machine learning techniques that are privacy-preserving. Given the increasing computing capabilities of modern edge de ...
