The Power of Encounters
A secure encounter is an agreement between two devices that they have met at a given time and place, and an associated shared secret. This secret enables the devices to subsequently authenticate each other, communicate securely, and prove their prior encounter without exchanging linkable information. In this talk, I will sketch how this simple idea enables fascinating new capabilities for mobile computing and the Internet-of-Things. We review opportunities and challenges associated with reliable, secure, and privacy-preserving mobile distributed computing based on encounters.
First, encounters enable secure communication among devices and users during and after a meeting, and without requiring users to share linkable information. Second, encounters enable powerful new forms of secure group communication among devices connected by chains of encounters, subject to spatial, temporal, and causality constraints. Applications range from connecting attendees of a large event and virtual guest books to disseminating health risk warnings, soliciting information and witnesses related to an incident, and tracing missing persons, all while protecting users' privacy. Third, encounters enable selective proofs of (co-)location of a set of devices at a given time and place. Finally, encounters promise proofs of unique physical trajectories, which can provide evidence that a device is controlled by a human user.
Peter Druschel is the founding director of the Max Planck Institute for Software Systems (MPI-SWS) in Germany. Previously, he was a Professor of Computer Science and Electrical and Computer Engineering at Rice University in Houston, Texas. His research interests include distributed systems, mobile systems, privacy and compliance. He is the recipient of an NSF CAREER Award, an Alfred P. Sloan Fellowship, the ACM SIGOPS Mark Weiser Award, a Microsoft Research Outstanding Collaborator Award, and the EuroSys Lifetime Achievement Award. Peter is a member of Academia Europaea and the German Academy of Sciences Leopoldina.
Recommanding Fast Data
Computing systems that make human sense of big data, usually called personalization systems or recommenders, and popularized by Amazon and Netflix, essentially help Internet users extracting information of interest to them. Leveraging machine learning techniques, research on personalization has mainly focused on improving the quality of the information extracted. Yet, building an operational recommender goes far beyond, especially in a world where data is not only big but also changes very fast. This talk will discuss system challenges to scale to a large number of users and a growing volume of fastly changing data to eventually provide real-time personalization.
Anne-Marie Kermarrec is a senior researcher at Inria, France where she led a research group on large-scale distributed systems from 2006 to 2015. She is the CEO of the Mediego startup that she founded in April 2015. Mediego provides content personalisation services for online publishers, and directly builds on her recent research. She got a PhD thesis from University of Rennes, and has been with Vrije Universiteit, NL and Microsoft Research Cambridge, UK. Anne-Marie received an ERC grant in 2008 and an ERC proof of Concept in 2013. She received the Montpetit Award in 2011 and the Innovation Award in 2017 from the French Academy of Science. She has been elected to the European Academy in 2013 and named ACM Fellow in 2016. Her research interests are large-scale distributed systems, peer to peer networks and system support for machine learning.