arXiv subjects

Gaurav Srivastava

Publications and source records attributed to Gaurav Srivastava.

23 records · Page 2Linked to original sources

arXiv2022

Fast Parallel Hypertree Decompositions in Logarithmic Recursion Depth

Modern trends in data collection are bringing current mainstream techniques for database query processing to their limits. Consequently, various novel approaches for efficient query processing are being actively studied. One such approach is based on hypertree decompositions (HDs), which have been shown to carry great potential to process complex queries more efficiently and with stronger theoretical guarantees. However, using HDs for query execution relies on the difficult task of computing decompositions of the query structure, which guides the efficient execution of the query. From theoretical results we know that the performance of purely sequential methods is inherently limited, yet the problem is susceptible to parallelisation. In this paper we propose the first algorithm for computing hypertree decompositions that is well-suited for parallelisation. The proposed algorithm log-k-decomp requires only a logarithmic number of recursion levels and additionally allows for highly parallelised pruning of the search space by restriction to balanced separators. We provide detailed experimental evaluation over the HyperBench benchmark and demonstrate that our approach is highly effective especially for complex queries.

cs.DB↗

arXiv2018

PrivacyProxy: Leveraging Crowdsourcing and In Situ Traffic Analysis to Detect and Mitigate Information Leakage

Many smartphone apps transmit personally identifiable information (PII), often without the users knowledge. To address this issue, we present PrivacyProxy, a system that monitors outbound network traffic and generates app-specific signatures to represent sensitive data being shared. PrivacyProxy uses a crowd-based approach to detect likely PII in an adaptive and scalable manner by anonymously combining signatures from different users of the same app. Furthermore, we do not observe users network traffic and instead rely on hashed signatures. We present the design and implementation of PrivacyProxy and evaluate it with a lab study, a field deployment, a user survey, and a comparison against prior work. Our field study shows PrivacyProxy can automatically detect PII with an F1 score of 0.885. PrivacyProxy also achieves an F1 score of 0.759 in our controlled experiment for the 500 most popular apps. The F1 score also improves to 0.866 with additional training data for 40 apps that initially had the most false positives. We also show performance overhead of using PrivacyProxy is between 8.6% to 14.2%, slightly more than using a standard unmodified VPN, and most users report no perceptible impact on battery life or the network.

cs.CR↗

arXiv2018

Minimizing Area and Energy of Deep Learning Hardware Design Using Collective Low Precision and Structured Compression

Deep learning algorithms have shown tremendous success in many recognition tasks; however, these algorithms typically include a deep neural network (DNN) structure and a large number of parameters, which makes it challenging to implement them on power/area-constrained embedded platforms. To reduce the network size, several studies investigated compression by introducing element-wise or row-/column-/block-wise sparsity via pruning and regularization. In addition, many recent works have focused on reducing precision of activations and weights with some reducing down to a single bit. However, combining various sparsity structures with binarized or very-low-precision (2-3 bit) neural networks have not been comprehensively explored. In this work, we present design techniques for minimum-area/-energy DNN hardware with minimal degradation in accuracy. During training, both binarization/low-precision and structured sparsity are applied as constraints to find the smallest memory footprint for a given deep learning algorithm. The DNN model for CIFAR-10 dataset with weight memory reduction of 50X exhibits accuracy comparable to that of the floating-point counterpart. Area, performance and energy results of DNN hardware in 40nm CMOS are reported for the MNIST dataset. The optimized DNN that combines 8X structured compression and 3-bit weight precision showed 98.4% accuracy at 20nJ per classification.

cs.NE↗

arXiv2015

Web application for size and topology optimization of trusses and gusset plates

With its ever growing popularity, providing Internet based applications tuned towards practical applications is on the rise. Advantages such as no external plugins and additional software, ease of use, updating and maintenance have increased the popularity of web applications. In this work, a web-based application has been developed which can perform size optimization of truss structure as a whole as well as topology optimization of individual gusset plate of each joint based on specified joint displacements and load conditions. This application is developed using cutting-edge web technologies such as Three.js and HTML5. The client side boasts of an intuitive interface which in addition to its modeling capabilities also recommends configurations based on user input, provides analysis options and finally displays the results. The server side, using a combination of Scilab and DAKOTA, computes solution and also provides the user with comparisons of the optimal design with that conforming to Indian Standard (IS 800-2007). It is a freely available one-stop web-based application to perform optimal and/or code based design of trusses.

cs.OH↗

arXiv2014

An Analysis of Random Projections in Cancelable Biometrics

With increasing concerns about security, the need for highly secure physical biometrics-based authentication systems utilizing \emph{cancelable biometric} technologies is on the rise. Because the problem of cancelable template generation deals with the trade-off between template security and matching performance, many state-of-the-art algorithms successful in generating high quality cancelable biometrics all have random projection as one of their early processing steps. This paper therefore presents a formal analysis of why random projections is an essential step in cancelable biometrics. By formally defining the notion of an \textit{Independent Subspace Structure} for datasets, it can be shown that random projection preserves the subspace structure of data vectors generated from a union of independent linear subspaces. The bound on the minimum number of random vectors required for this to hold is also derived and is shown to depend logarithmically on the number of data samples, not only in independent subspaces but in disjoint subspace settings as well. The theoretical analysis presented is supported in detail with empirical results on real-world face recognition datasets.

cs.CV↗