Where Have You Been? Secure Location Provenance for Mobile Devices

With the advent of mobile computing, location-based services have recently gained popularity. Many applications use the location provenance of users, i.e., the chronological history of the users' location for purposes ranging from access control, authentication, information sharing, and evaluation of policies. However, location provenance is subject to tampering and collusion attacks by malicious users. In this paper, we examine the secure location provenance problem. We introduce a witness-endorsed scheme for generating collusion-resistant location proofs. We also describe two efficient and privacy-preserving schemes for protecting the integrity of the chronological order of location proofs. These schemes, based on hash chains and Bloom filters respectively, allow users to prove the order of any arbitrary subsequence of their location history to auditors. Finally, we present experimental results from our proof-of-concept implementation on the Android platform and show that our schemes are practical in today's mobile devices.Comment: 14 page

LifeRaft: Data-Driven, Batch Processing for the Exploration of Scientific Databases

Workloads that comb through vast amounts of data are gaining importance in the sciences. These workloads consist of "needle in a haystack" queries that are long running and data intensive so that query throughput limits performance. To maximize throughput for data-intensive queries, we put forth LifeRaft: a query processing system that batches queries with overlapping data requirements. Rather than scheduling queries in arrival order, LifeRaft executes queries concurrently against an ordering of the data that maximizes data sharing among queries. This decreases I/O and increases cache utility. However, such batch processing can increase query response time by starving interactive workloads. LifeRaft addresses starvation using techniques inspired by head scheduling in disk drives. Depending upon the workload saturation and queuing times, the system adaptively and incrementally trades-off processing queries in arrival order and data-driven batch processing. Evaluating LifeRaft in the SkyQuery federation of astronomy databases reveals a two-fold improvement in query throughput.Comment: CIDR 200

Optimize Unsynchronized Garbage Collection in an SSD Array

Solid state disks (SSDs) have advanced to outperform traditional hard drives significantly in both random reads and writes. However, heavy random writes trigger fre- quent garbage collection and decrease the performance of SSDs. In an SSD array, garbage collection of individ- ual SSDs is not synchronized, leading to underutilization of some of the SSDs. We propose a software solution to tackle the unsyn- chronized garbage collection in an SSD array installed in a host bus adaptor (HBA), where individual SSDs are exposed to an operating system. We maintain a long I/O queue for each SSD and flush dirty pages intelligently to fill the long I/O queues so that we hide the performance imbalance among SSDs even when there are few parallel application writes. We further define a policy of select- ing dirty pages to flush and a policy of taking out stale flush requests to reduce the amount of data written to SSDs. We evaluate our solution in a real system. Experi- ments show that our solution fully utilizes all SSDs in an array under random write-heavy workloads. It improves I/O throughput by up to 62% under random workloads of mixed reads and writes when SSDs are under active garbage collection. It causes little extra data writeback and increases the cache hit rate

Active Community Detection in Massive Graphs

A canonical problem in graph mining is the detection of dense communities. This problem is exacerbated for a graph with a large order and size -- the number of vertices and edges -- as many community detection algorithms scale poorly. In this work we propose a novel framework for detecting active communities that consist of the most active vertices in massive graphs. The framework is applicable to graphs having billions of vertices and hundreds of billions of edges. Our framework utilizes a parallelizable trimming algorithm based on a locality statistic to filter out inactive vertices, and then clusters the remaining active vertices via spectral decomposition on their similarity matrix. We demonstrate the validity of our method with synthetic Stochastic Block Model graphs, using Adjusted Rand Index as the performance metric. We further demonstrate its practicality and efficiency on a most recent real-world Hyperlink Web graph consisting of over 3.5 billion vertices and 128 billion edges.Comment: published in SDM-Networks 201

The Life and Death of Unwanted Bits: Towards Proactive Waste Data Management in Digital Ecosystems

Our everyday data processing activities create massive amounts of data. Like physical waste and trash, unwanted and unused data also pollutes the digital environment by degrading the performance and capacity of storage systems and requiring costly disposal. In this paper, we propose using the lessons from real life waste management in handling waste data. We show the impact of waste data on the performance and operational costs of our computing systems. To allow better waste data management, we define a waste hierarchy for digital objects and provide insights into how to identify and categorize waste data. Finally, we introduce novel ways of reusing, reducing, and recycling data and software to minimize the impact of data wastageComment: Fixed reference

Gradient-Domain Processing for Large EM Image Stacks

We propose a new gradient-domain technique for processing registered EM image stacks to remove the inter-image discontinuities while preserving intra-image detail. To this end, we process the image stack by first performing anisotropic diffusion to smooth the data along the slice axis and then solving a screened-Poisson equation within each slice to re-introduce the detail. The final image stack is both continuous across the slice axis (facilitating the tracking of information between slices) and maintains sharp details within each slice (supporting automatic feature detection). To support this editing, we describe the implementation of the first multigrid solver designed for efficient gradient domain processing of large, out-of-core, voxel grids

knor: A NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means Library

k-means is one of the most influential and utilized machine learning algorithms. Its computation limits the performance and scalability of many statistical analysis and machine learning tasks. We rethink and optimize k-means in terms of modern NUMA architectures to develop a novel parallelization scheme that delays and minimizes synchronization barriers. The \textit{k-means NUMA Optimized Routine} (\textsf{knor}) library has (i) in-memory (\textsf{knori}), (ii) distributed memory (\textsf{knord}), and (iii) semi-external memory (\textsf{knors}) modules that radically improve the performance of k-means for varying memory and hardware budgets. \textsf{knori} boosts performance for single machine datasets by an order of magnitude or more. \textsf{knors} improves the scalability of k-means on a memory budget using SSDs. \textsf{knors} scales to billions of points on a single machine, using a fraction of the resources that distributed in-memory systems require. \textsf{knord} retains \textsf{knori}'s performance characteristics, while scaling in-memory through distributed computation in the cloud. \textsf{knor} modifies Elkan's triangle inequality pruning algorithm such that we utilize it on billion-point datasets without the significant memory overhead of the original algorithm. We demonstrate \textsf{knor} outperforms distributed commercial products like H$_2$O, Turi (formerly Dato, GraphLab) and Spark's MLlib by more than an order of magnitude for datasets of $10^7$ to $10^9$ points

FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs

R is one of the most popular programming languages for statistics and machine learning, but the R framework is relatively slow and unable to scale to large datasets. The general approach for speeding up an implementation in R is to implement the algorithms in C or FORTRAN and provide an R wrapper. FlashR takes a different approach: it executes R code in parallel and scales the code beyond memory capacity by utilizing solid-state drives (SSDs) automatically. It provides a small number of generalized operations (GenOps) upon which we reimplement a large number of matrix functions in the R base package. As such, FlashR parallelizes and scales existing R code with little/no modification. To reduce data movement between CPU and SSDs, FlashR evaluates matrix operations lazily, fuses operations at runtime, and uses cache-aware, two-level matrix partitioning. We evaluate FlashR on a variety of machine learning and statistics algorithms on inputs of up to four billion data points. FlashR out-of-core tracks closely the performance of FlashR in-memory. The R code for machine learning algorithms executed in FlashR outperforms the in-memory execution of H2O and Spark MLlib by a factor of 2-10 and outperforms Revolution R Open by more than an order of magnitude

Gradient-Domain Fusion for Color Correction in Large EM Image Stacks

We propose a new gradient-domain technique for processing registered EM image stacks to remove inter-image discontinuities while preserving intra-image detail. To this end, we process the image stack by first performing anisotropic smoothing along the slice axis and then solving a Poisson equation within each slice to re-introduce the detail. The final image stack is continuous across the slice axis and maintains sharp details within each slice. Adapting existing out-of-core techniques for solving the linear system, we describe a parallel algorithm with time complexity that is linear in the size of the data and space complexity that is sub-linear, allowing us to process datasets as large as five teravoxels with a 600 MB memory footprint

An SSD-based eigensolver for spectral analysis on billion-node graphs

Many eigensolvers such as ARPACK and Anasazi have been developed to compute eigenvalues of a large sparse matrix. These eigensolvers are limited by the capacity of RAM. They run in memory of a single machine for smaller eigenvalue problems and require the distributed memory for larger problems. In contrast, we develop an SSD-based eigensolver framework called FlashEigen, which extends Anasazi eigensolvers to SSDs, to compute eigenvalues of a graph with hundreds of millions or even billions of vertices in a single machine. FlashEigen performs sparse matrix multiplication in a semi-external memory fashion, i.e., we keep the sparse matrix on SSDs and the dense matrix in memory. We store the entire vector subspace on SSDs and reduce I/O to improve performance through caching the most recent dense matrix. Our result shows that FlashEigen is able to achieve 40%-60% performance of its in-memory implementation and has performance comparable to the Anasazi eigensolvers on a machine with 48 CPU cores. Furthermore, it is capable of scaling to a graph with 3.4 billion vertices and 129 billion edges. It takes about four hours to compute eight eigenvalues of the billion-node graph using 120 GB memory