AT&T Labs

About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.

Vis-à-Vis: Privacy-preserving online social networking via Virtual Individual Servers

Abstract: Online social networks (OSNs) are immensely popular, but their centralized control of user data raises important privacy concerns. This paper presents Vis-a-Vis, a decentralized framework for OSNs based on the privacy-preserving notion of a Virtual Individual Server (VIS). A VIS is a personal virtual machine running in a paid compute utility. In Vis-a-Vis, a person stores her data on her own VIS, which arbitrates access to that data by others. VISs self-organize into overlay networks corresponding to social groups. This paper focuses on preserving the privacy of location information. Vis-a-Vis uses distributed location trees to provide efficient and scalable operations for sharing location information within social groups. We have evaluated our Vis-a-Vis prototype using hundreds of virtual machines running in the Amazon EC2 compute utility. Our results demonstrate that Vis-a-Vis represents an attractive complement to today's centralized OSNs.

Beyond the Descartes Circle Theorem

Abstract: (2002). Beyond the Descartes Circle Theorem. The American Mathematical Monthly: Vol. 109, No. 4, pp. 338-361.

Teen use of messaging media

Abstract: Teenagers compromise a large proportion of our population, and their technology use is a bellwether of future trends. Today's teens are coming of age with the rapid development of advanced communication and media tools. This paper describes a study exploring teen communication media usage patterns and their design implications.

The Power of Simple Tabulation Hashing

Abstract: Randomized algorithms are often enjoyed for their simplicity, but the hash functions used to yield the desired theoretical guarantees are often neither simple nor practical. Here we show that the simplest possible tabulation hashing provides unexpectedly strong guarantees.The scheme itself dates back to Zobrist in 1970 who used it for game playing programs. Keys are viewed as consisting of c characters. We initialize c tables H1, ..., Hc mapping characters to random hash codes. A key x = (x1, ..., xc) is hashed to H1[x1] ⊕ c ⊕ Hc[xc], where ⊕ denotes bit-wise exclusive-or.While this scheme is not even 4-independent, we show that it provides many of the guarantees that are normally obtained via higher independence, for example, Chernoff-type concentration, min-wise hashing for estimating set intersection, and cuckoo hashing.

Crowd++: unsupervised speaker count with smartphones

Abstract: Smartphones are excellent mobile sensing platforms, with the microphone in particular being exercised in several audio inference applications. We take smartphone audio inference a step further and demonstrate for the first time that it's possible to accurately estimate the number of people talking in a certain place -- with an average error distance of 1.5 speakers -- through unsupervised machine learning analysis on audio segments captured by the smartphones. Inference occurs transparently to the user and no human intervention is needed to derive the classification model. Our results are based on the design, implementation, and evaluation of a system called Crowd++, involving 120 participants in 10 very different environments. We show that no dedicated external hardware or cumbersome supervised learning approaches are needed but only off-the-shelf smartphones used in a transparent manner. We believe our findings have profound implications in many research fields, including social sensing and personal wellbeing assessment.