Sean Chester

Anonymizing Subsets of Social Networks with Degree Constrained Subgraphs

In recent years, concerns of privacy have become more prominent for social networks. Anonymizing a graph meaningfully is a challenging problem, as the original graph properties must be preserved as well as possible. We introduce a generalization of the degree anonymization problem posed by Liu and Terzi. In this problem, our goal is to anonymize a given subset of nodes while adding the fewest possible number of edges. The main contribution of this paper is an efficient algorithm for this problem by exploring its connection with the degree-constrained subgraph problem. Our experimental results show that our algorithm performs very well on many instances of social network data.

Social Network Privacy for Attribute Disclosure Attacks

Increasing research on social networks stresses the urgency for producing effective means of ensuring user privacy. Represented ubiquitously as graphs, social networks have a myriad of recently developed techniques to prevent identity disclosure, but the equally important attribute disclosure attacks have been neglected. To address this gap, we introduce an approach to anonymize social networks that have labeled nodes,

Work-efficient parallel skyline computation for the GPU

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Scalable parallelization of skyline computation for multi-core processors

-proximity, which requires that the label distribution in every neighbourhood of the graph be close to that throughout the entire network. We present an effective greedy algorithm to achieve

Computing k-regret minimizing sets

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Indexing Reverse Top-k Queries in Two Dimensions

-proximity and experimentally validate the quality of the solutions it derives.

Indexing for vector projections

The skyline operator returns records in a dataset that provide optimal trade-offs of multiple dimensions. State-of-the-art skyline computation involves complex tree traversals, data-ordering, and conditional branching to minimize the number of point-to-point comparisons. Meanwhile, GPGPU computing offers the potential for parallelizing skyline computation across thousands of cores. However, attempts to port skyline algorithms to the GPU have prioritized throughput and failed to outperform sequential algorithms. In this paper, we introduce a new skyline algorithm, designed for the GPU, that uses a global, static partitioning scheme. With the partitioning, we can permit controlled branching to exploit transitive relationships and avoid most point-to-point comparisons. The result is a non-traditional GPU algorithm, SkyAlign, that prioritizes work-efficiency and respectable throughput, rather than maximal throughput, to achieve orders of magnitude faster performance.

Hashcube: A Data Structure for Space- and Query-Efficient Skycube Compression

The skyline is an important query operator for multi-criteria decision making. It reduces a dataset to only those points that offer optimal trade-offs of dimensions. In general, it is very expensive to compute. Recently, multicore CPU algorithms have been proposed to accelerate the computation of the skyline. However, they do not sufficiently minimize dominance tests and so are not competitive with state-of-the-art sequential algorithms. In this paper, we introduce a novel multicore skyline algorithm, Hybrid, which processes points in blocks. It maintains a shared, global skyline among all threads, which is used to minimize dominance tests while maintaining high throughput. The algorithm uses an efficiently-updatable data structure over the shared, global skyline, based on point-based partitioning. Also, we release a large benchmark of optimized skyline algorithms, with which we demonstrate on challenging workloads a 100-fold speedup over state-of-the-art multicore algorithms and a 10-fold speedup with 16 cores over state-of-the-art sequential algorithms.

Scalable APRIORI-Based Frequent Pattern Discovery

Regret minimizing sets are a recent approach to representing a dataset D by a small subset R of size r of representative data points. The set R is chosen such that executing any top-1 query on R rather than D is minimally perceptible to any user. However, such a subset R may not exist, even for modest sizes, r. In this paper, we introduce the relaxation to k-regret minimizing sets, whereby a top-1 query on R returns a result imperceptibly close to the top-k on D. We show that, in general, with or without the relaxation, this problem is NP-hard. For the specific case of two dimensions, we give an efficient dynamic programming, plane sweep algorithm based on geometric duality to find an optimal solution. For arbitrary dimension, we give an empirically effective, greedy, randomized algorithm based on linear programming. With these algorithms, we can find subsets R of much smaller size that better summarize D, using small values of k larger than 1.

SkyAlign: a portable, work-efficient skyline algorithm for multicore and GPU architectures

We consider the recently introduced monochromatic reverse top−k query which asks for, given a (possibly new) tuple q and a dataset \(\mathcal{D}\), all possible top−k queries on \(\mathcal{D}\cup\{q\}\) for which q is in the result. Towards this problem, we introduce the first query-agnostic approach, which leads to an efficient index. We present the novel insight that by representing the dataset as an arrangement of lines, a critical k-polygon can be identified and can singularly answer reverse top−k queries.