This talk gives some initial thoughts on how to make a synthetic network model from private contact networks.
This talk is based around the Transactions on Networking paper. We use 232 traffic traces to establish that for "mid-large" internet link (backbone links or ingress/egress links from reasonable sized institutions) the traffic is well-modelled by a log-normal distribution.
The associated paper is here:
This paper is a presentation of the FETA framework and new work with Naomi Arnold on time varying models.
This paper updates previous work on fitting traffic profiles. We use more modern statistical techniques to question (and refute) previous assumptions about heavy tails in statistics. In this case we believe that the best fit for traffic volume per unit time is the log-normal distribution. Tail distributions an have big impacts for capacity planning and for prediction of pricing (say 95th percentile).
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.
This paper describes the Raphtory system which is used to analysis large-scale time-varying graph systems. It can ingest streaming graph information and store the complete graph history. It enables queries to be made over the graphs at different points in that graph's history.