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.

Personal privacy vs population privacy: learning to attack anonymization

Abstract: Over the last decade great strides have been made in developing techniques to compute functions privately. In particular, Differential Privacy gives strong promises about conclusions that can be drawn about an individual. In contrast, various syntactic methods for providing privacy (criteria such as k-anonymity and l-diversity) have been criticized for still allowing private information of an individual to be inferred. In this paper, we consider the ability of an attacker to use data meeting privacy definitions to build an accurate classifier. We demonstrate that even under Differential Privacy, such classifiers can be used to infer "private" attributes accurately in realistic data. We compare this to similar approaches for inference-based attacks on other forms of anonymized data. We show how the efficacy of all these attacks can be measured on the same scale, based on the probability of successfully inferring a private attribute. We observe that the accuracy of inference of private attributes for differentially private data and $l$-diverse data can be quite similar.

Exact Solutions to Time-Dependent MDPs

Abstract: We describe an extension of the Markov decision process model in which a continuous time dimension is included in the state space. This allows for the representation and exact solution of a wide range of problems in which transitions or rewards vary over time. We examine problems based on route planning with public transportation and telescope observation scheduling.

UWB indoor path loss model for residential and commercial buildings

Abstract: We present a statistical model for the path loss of ultra-wideband channels in indoor environments. In contrast to previous measurements, the data reported here are for a bandwidth of 6 GHz rather than 1.25 GHz; they encompass commercial buildings in addition to single-family homes (20 of each); and local spatial averaging is included. As before, the center frequency is 5.0 GHz. Separate models are given for commercial and residential environments and-within each category-for line-of-sight (LOS) and non-line-of-sight (NLS) paths. All four models have the same mathematical structure, differing only in their numerical parameters. The two new models (LOS and NLS) for residences closely match those derived from the previous measurements, thus affirming the stability of our path loss modeling. For greater accuracy, we therefore pool the two data sets in our final models for residences. We find that the path loss statistics for the two categories of buildings are quite similar.

Tap-selectable decision-feedback equalization

Abstract: This paper presents an adaptive tap assignment technique for improving the performance of a previously reported reduced-complexity decision-feedback equalizer (DFE) for broadband band wireless systems. The spacings of individual feedforward taps of the DFE are made selectable so that, when the channel consists of a sparsely distributed multipath with a large delay spread (e.g. "hilly terrain" (HT) delay profiles), the equalizer span can be increased without increasing the total number of taps. We propose simple tap selection algorithms and show that they provide: (1) performance gains over a contiguous-tap approach in various outdoor delay profiles and (2) improved robustness against fast fading.

Multiple aggregations over data streams

Abstract: Monitoring aggregates on IP traffic data streams is a compelling application for data stream management systems. The need for exploratory IP traffic data analysis naturally leads to posing related aggregation queries on data streams, that differ only in the choice of grouping attributes. In this paper, we address this problem of efficiently computing multiple aggregations over high speed data streams, based on a two-level LFTA/HFTA DSMS architecture, inspired by Gigascope.Our first contribution is the insight that in such a scenario, additionally computing and maintaining fine-granularity aggregation queries (phantoms) at the LFTA has the benefit of supporting shared computation. Our second contribution is an investigation into the problem of identifying beneficial LFTA configurations of phantoms and user-queries. We formulate this problem as a cost optimization problem, which consists of two sub-optimization problems: how to choose phantoms and how to allocate space for them in the LFTA. We formally show the hardness of determining the optimal configuration, and propose cost greedy heuristics for these independent sub-problems based on detailed analyses. Our final contribution is a thorough experimental study, based on real IP traffic data, as well as synthetic data, to demonstrate the effectiveness of our techniques for identifying beneficial configurations.