Mobile Sensor Data Anonymization

Workshop paper
ACM/IEEE International Conference on Internet-of-Things Design and Implementation

This paper looks the problem of releasing time-series data when privacy is a concern. It uses information theory to look at what extra information could "leak" if our device sends motion data. For example, can users be reidentified or can features such as height and weight be determined. A machine learning framework is given that can produce a tradeoff between allowing useful data to pass through while distorting the signal minimally to disguise information we wish to be private.

On rate limitation mechanisms for TCP throughput: a longitudinal analysis

Journal Paper
Computer Networks

This paper is a considerably expanded version of the INFOCOM paper.

Again it argues that TCP is no longer mainly controlled by loss and congestion but instead by algorithms and settings under the control of the sender or receiver deliberately or accidentally designed to restrict throughput for a variety of reasons (for example limiting video sending to the rate at which the viewer is watching).

It contains extended discussion of the methodology and in particular how flight and RTT data was extracted from passive traces.

Likelihood-based assessment of dynamic networks

Journal Paper
Journal of Complex Networks

This paper used a likelihood based framework to create a rigorous way to assess models of networks. Network evolution is broken down into an operation model (it decides the 'type' of change to be made to the network, e.g. "add node" "add link" "remove node" "remove link") and an object model (that decides the exact change -- which node/link to add).

The system is shown to be able to recover known parameters on artificial models and to be useful in analysis of real data.


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