Microsoft Technology Licensing, LLC

Privitar Limited

George Danezis

Emre Mehmet Kiciman

Shuo Chen

Tuomas Aura

Jason Derek McFall

Markulf Kohlweiss

Charles Codman Cabot

Benjamin Thomas Pickering

Theresa Stadler

Kieron Francois Pascal Guinamard

Paul David Mellor

Vladimir Michael Eatwell

Timothy James Moran

Christopher Andrew Smith

Anthony Jason Du Preez

Igor Vujosevic

Andrei Petre

A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.

Computer-implemented privacy engineering system and method

Sharing of user preferences is described. In an embodiment a user preference associated with a user is shared with a group of users in order to improve the relevance of results they receive. A database is used to store information detailing a number of groups of users extracted from a social network graph, where the social network graph describes connections between users. On receipt of a user preference associated with a user, a group of users containing the user is selected and the user preference is then shared with everyone in the selected group. In a further embodiment, the groups of users in the database may comprise cohesive groups of users and an extended group associated with each cohesive group. When selecting a group to share preference data with, a cohesive group containing the user is first selected and then the preference data is shared with the corresponding extended group.

Sharing of user preferences

Privacy-preserving metering with low overhead is described. In an embodiment consumption of a resource such as electricity, car insurance, cloud computing resources is monitored by a meter and bills are created in a manner which preserves privacy of a customer but at the same reduces bandwidth use between a meter and a provider of the resource. For example, fine grained meter readings which describe customer behavior are kept confidential without needing to send large cryptographic commitments to meter readings from a meter to a provider. In an example, meter readings are encrypted and sent from a meter to a provider who is unable to decrypt the readings. In examples a cryptographic signature is generated to commitments to the meter readings and only the signature is sent to a provider thus reducing bandwidth. For example, a customer device is able to regenerate the commitments using the signature.

Privacy-preserving metering with low overhead

Growing community of inventors

Cambridge, United Kingdom

George Danezis