Carnegie Mellon University

About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Computer science & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.

The TESLA Broadcast Authentication Protocol

Abstract: One of the main challenges of securing broadcast communication is source authentication, or enabling receivers of broadcast data to verify that the received data really originates from the claimed source and was not modified en route. This problem is complicated by mutually untrusted receivers and unreliable communication environments where the sender does not retransmit lost packets. This article presents the TESLA (Timed Efficient Stream Loss-tolerant Authentication) broadcast authentication protocol, an efficient protocol with low communication and computation overhead, which scales to large numbers of receivers, and tolerates packet loss. TESLA is based on loose time synchronization between the sender and the receivers. Despite using purely symmetric cryptographic functions (MAC functions), TESLA achieves asymmetric properties. We discuss a PKI application based purely on TESLA, assuming that all network nodes are loosely time synchronized.

A framework for the interpretation of de novo mutation in human disease

Abstract: Mark Daly and colleagues present a statistical framework to evaluate the role of de novo mutations in human disease by calibrating a model of de novo mutation rates at the individual gene level. The mutation probabilities defined by their model and list of constrained genes can be used to help identify genetic variants that have a significant role in disease.

Practical privacy: the SuLQ framework

Abstract: We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping database rows to {0, 1}. The true answer is ΣieSf(di), and a noisy version is released as the response to the query. Results of Dinur, Dwork, and Nissim show that a strong form of privacy can be maintained using a surprisingly small amount of noise -- much less than the sampling error -- provided the total number of queries is sublinear in the number of database rows. We call this query and (slightly) noisy reply the SuLQ (Sub-Linear Queries) primitive. The assumption of sublinearity becomes reasonable as databases grow increasingly large.We extend this work in two ways. First, we modify the privacy analysis to real-valued functions f and arbitrary row types, as a consequence greatly improving the bounds on noise required for privacy. Second, we examine the computational power of the SuLQ primitive. We show that it is very powerful indeed, in that slightly noisy versions of the following computations can be carried out with very few invocations of the primitive: principal component analysis, k means clustering, the Perceptron Algorithm, the ID3 algorithm, and (apparently!) all algorithms that operate in the in the statistical query learning model [11].

Net-zero emissions energy systems

Abstract: Some energy services and industrial processes-such as long-distance freight transport, air travel, highly reliable electricity, and steel and cement manufacturing-are particularly difficult to provide without adding carbon dioxide (CO2) to the atmosphere. Rapidly growing demand for these services, combined with long lead times for technology development and long lifetimes of energy infrastructure, make decarbonization of these services both essential and urgent. We examine barriers and opportunities associated with these difficult-to-decarbonize services and processes, including possible technological solutions and research and development priorities. A range of existing technologies could meet future demands for these services and processes without net addition of CO2 to the atmosphere, but their use may depend on a combination of cost reductions via research and innovation, as well as coordinated deployment and integration of operations across currently discrete energy industries.