The increasing availability and use of genome data for applications like personalized medicine have created opportunities for the improved diagnosis and treatment of various medical conditions. However, it has a potential to be used for discrimination, thereby presenting a set of serious challenges in privacy and security. We propose a secure evaluation algorithm to compute genomic tests that are based on a linear combination of genome data values (we use the Warfarin dosing algorithm as a representative example). Our proposal relies on a combination of partially homomorphic Paillier encryption and private information retrieval. We implement a prototype system that includes the Paillier encryption part of our protocol. Our initial evaluation demonstrates a good potential for real time use in a physicianpatient scenario, with a response time of around 200ms in a Wi-Fi communications environment.
Network outages are an important issue for Internet Service Providers (ISPs) and, more generally, online service providers, as they can result in major financial losses and negatively impact relationships with their customers. Troubleshooting network outages is a complex and time-consuming process. Network administrators are overwhelmed with large volumes of monitoring data and are limited to using very basic tools for debugging, e.g., ping and traceroute. Intelligent correlation of measurements from different Internet locations is very useful for analyzing the root cause of outages. However, correlating measurements of user traffic across domains is largely avoided as it raises privacy concerns. A possible solution is secure multi-party computation (MPC), a set of cryptographic methods that enable a number of parties to aggregate data in a privacy-preserving manner. In this work, we describe a novel system that helps diagnose network outages by correlating passive measurements from multiple ISPs in a privacy-preserving manner. We first show how MPC can be used to compute the scope (local, global, or semi-global) and severity (number of affected hosts) of network outages. To meet near-real-time monitoring guarantees, we then present an efficient protocol for MPC multiset union that uses counting Bloom filters (CBF) to drastically accelerate MPC comparison operations. Finally, we demonstrate the utility of our scheme using real-world traffic measurements from a national ISP and we discuss the trade-offs of the CBF-based computation.
There is an abundance of intelligent tutors and expert systems across all domains. Some are driven by "cognitive technologies" and conversational agents, which have become common in providing an accessible interface to a range of devices. They are often used to provision initial support for businesses to guide users of their websites through frequently asked questions and help material. However, there are only few examples applied to the educational context, and Massive Open Online Courses (MOOCs) in particular. This paper presents work in progress aiming to bring cognitive technologies to use in the running of MOOCs and supporting both instructors and students to answer content-related questions. The overall architecture of the system is presented, together with the modeling of the knowledge base in three different MOOCs from different disciplines. The approach is promising in offering a tool capable of providing teacher support, as well as direct student support, in dealing with the large amount of questions posted in discussion forums and in answering common questions regarding both the running of the course as well as content.
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