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  • Author: Alastair R. Beresford x
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Open access

Stephan A. Kollmann, Martin Kleppmann and Alastair R. Beresford

Abstract

Document collaboration applications, such as Google Docs or Microsoft Office Online, need to ensure that all collaborators have a consistent view of the shared document, and usually achieve this by relying on a trusted server. Other existing approaches that do not rely on a trusted third party assume that all collaborating devices are trusted. In particular, when inviting a new collaborator to a group, one needs to choose between a) keeping past edits private and sending only the latest state (a snapshot) of the document; or b) allowing the new collaborator to verify her view of the document is consistent with other honest devices by sending the full history of (signed) edits. We present a new protocol which allows an authenticated snapshot to be sent to new collaborators while both hiding the past editing history, and allowing them to verify consistency. We evaluate the costs of the protocol by emulating the editing history of 270 Wikipedia pages; 99% of insert operations were processed within 11.0 ms; 64.9 ms for delete operations. An additional benefit of authenticated snapshots is a median 84% reduction in the amount of data sent to a new collaborator compared to a basic protocol that transfers a full edit history.

Open access

Dionysis Manousakas, Cecilia Mascolo, Alastair R. Beresford, Dennis Chan and Nikhil Sharma

Abstract

Human mobility is often represented as a mobility network, or graph, with nodes representing places of significance which an individual visits, such as their home, work, places of social amenity, etc., and edge weights corresponding to probability estimates of movements between these places. Previous research has shown that individuals can be identified by a small number of geolocated nodes in their mobility network, rendering mobility trace anonymization a hard task. In this paper we build on prior work and demonstrate that even when all location and timestamp information is removed from nodes, the graph topology of an individual mobility network itself is often uniquely identifying. Further, we observe that a mobility network is often unique, even when only a small number of the most popular nodes and edges are considered. We evaluate our approach using a large dataset of cell-tower location traces from 1 500 smartphone handsets with a mean duration of 430 days. We process the data to derive the top−N places visited by the device in the trace, and find that 93% of traces have a unique top−10 mobility network, and all traces are unique when considering top−15 mobility networks. Since mobility patterns, and therefore mobility networks for an individual, vary over time, we use graph kernel distance functions, to determine whether two mobility networks, taken at different points in time, represent the same individual. We then show that our distance metrics, while imperfect predictors, perform significantly better than a random strategy and therefore our approach represents a significant loss in privacy.