Biblio

Today’s data is so huge so it’s referred to as “Big data.” Such data now exceeds petabytes, and hence businesses have begun to store it in the cloud. Because the cloud is a third party, data must be secured before being uploaded to the cloud in such a way that cloud mining may be performed on protected data, as desired by the organization. Homomorphic encryption permits mining and analysis of encrypted data, hence it is used in the proposed work to encrypt original data on the data owner’s site. Since, homomorphic encryption is a complicated encryption, it takes a long time to encrypt, causing performance to suffer. So, in this paper, we used Hadoop to implement homomorphic encryption, which splits data across nodes in a Hadoop cluster to execute parallel algorithm and provides greater privacy and performance than previous approaches. It also enables for data mining in encrypted form, ensuring that the cloud never sees the original data during mining.

K-Nearest Neighbor (k-NN) search is one of the most commonly used approaches for similarity search. It finds extensive applications in machine learning and data mining. This era of big data warrants efficiently scaling k-NN search algorithms for billion-scale datasets with high dimensionality. In this paper, we propose a solution towards this end where we use vantage point trees for partitioning the dataset across multiple processes and exploit an existing graph-based sequential approximate k-NN search algorithm called HNSW (Hierarchical Navigable Small World) for searching locally within a process. Our hybrid MPI-OpenMP solution employs techniques including exploiting MPI one-sided communication for reducing communication times and partition replication for better load balancing across processes. We demonstrate computation of k-NN for 10,000 queries in the order of seconds using our approach on 8000 cores on a dataset with billion points in an 128-dimensional space. We also show 10X speedup over a completely k-d tree-based solution for the same dataset, thus demonstrating better suitability of our solution for high dimensional datasets. Our solution shows almost linear strong scaling.

The Moving Picture Experts Group's Compact Descriptors for Visual Search (MPEG's CDVS) intends to standardize technologies in order to enable an interoperable, efficient, and cross-platform solution for internet-scale visual search applications and services. Among the key technologies within CDVS, we recall the format of visual descriptors, the descriptor extraction process, and the algorithms for indexing and matching. Unfortunately, these steps require precision and computation accuracy. Moreover, they are very time-consuming, as they need running times in the order of seconds when implemented on the central processing unit (CPU) of modern mobile devices. In this paper, to reduce computation times and maintain precision and accuracy, we re-design, for many-cores embedded graphical processor units (GPUs), all main local descriptor extraction pipeline phases of the MPEG's CDVS standard. To reach this goal, we introduce new techniques to adapt the standard algorithm to parallel processing. Furthermore, to reduce memory accesses and efficiently distribute the kernel workload, we use new approaches to store and retrieve CDVS information on proper GPU data structures. We present a complete experimental analysis on a large and standard test set. Our experiments show that our GPU-based approach is remarkably faster than the CPU-based reference implementation of the standard, and it maintains a comparable precision in terms of true and false positive rates.

{Surrogate model-based optimization algorithm remains as an important solution to expensive black-box function optimization. The introduction of ensemble model enables the algorithm to automatically choose a proper model integration mode and adapt to various parameter spaces when dealing with different problems. However, this also significantly increases the computational burden of the algorithm. On the other hand, utilizing parallel computing resources and improving efficiency of black-box function optimization also require combination with surrogate optimization algorithm in order to design and realize an efficient parallel parameter space sampling mechanism. This paper makes use of parallel computing technology to speed up the weight updating related computation for the ensemble model based on Dempster-Shafer theory, and combines it with stochastic response surface method to develop a novel parallel sampling mechanism for asynchronous parameter optimization. Furthermore, it designs and implements corresponding parallel computing framework and applies the developed algorithm to quantitative trading strategy tuning in financial market. It is verified that the algorithm is both feasible and effective in actual application. The experiment demonstrates that with guarantee of optimizing performance, the parallel optimization algorithm can achieve excellent accelerating effect.

The network attack graph is a powerful tool for analyzing network security, but the generation of a large-scale graph is non-trivial. The main challenge is from the explosion of network state space, which greatly increases time and storage costs. In this paper, three parallel algorithms are proposed to generate scalable attack graphs. An OpenMP-based programming implementation is used to test their performance. Compared with the serial algorithm, the best performance from the proposed algorithms provides a 10X speedup.

In the business and research landscape of today, data analysis consumes public and proprietary data from numerous sources, and utilizes any one or more of popular data-parallel frameworks such as Hadoop, Spark and Flink. In the Data Lake setting these frameworks co-exist. Our earlier work has shown that data provenance in Data Lakes can aid with both traceability and management. The sheer volume of fine-grained provenance generated in a multi-framework application motivates the need for on-the-fly provenance processing. We introduce a new parallel stream processing algorithm that reduces fine-grained provenance while preserving backward and forward provenance. The algorithm is resilient to provenance events arriving out-of-order. It is evaluated using several strategies for partitioning a provenance stream. The evaluation shows that the parallel algorithm performs well in processing out-of-order provenance streams, with good scalability and accuracy.

Fast, accurate three dimensional reconstructions of plasma equilibria, crucial for physics interpretation of fusion data generated within confinement devices like stellarators/ tokamaks, are computationally very expensive and routinely require days, even weeks, to complete using serial approaches. Here, we present a parallel implementation of the three dimensional plasma reconstruction code, V3FIT. A formal analysis to identify the performance bottlenecks and scalability limits of this new parallel implementation, which combines both task and data parallelism, is presented. The theoretical findings are supported by empirical performance results on several thousands of processor cores of a Cray XC30 supercomputer. Parallel V3FIT is shown to deliver over 40X speedup, enabling fusion scientists to carry out three dimensional plasma equilibrium reconstructions at unprecedented scales in only a few hours (instead of in days/weeks) for the first time.

Elliptic curve asymmetric cryptography has achieved increased popularity due to its capability of providing comparable levels of security as other existing cryptographic systems while requiring less computational work. Pollard Rho and Parallel Collision Search, the fastest known sequential and parallel algorithms for breaking this cryptographic system, have been successfully applied over time to break ever-increasing bit-length system instances using implementations heavily optimized for the available hardware. This work presents portable, general implementations of a Parallel Collision Search based solution for prime elliptic curve asymmetric cryptographic systems that use publicly available big integer libraries and make no assumption on prime curve properties. It investigates which bit-length keys can be broken in reasonable time by a user that has access to a state of the art, public HPC equipment with CPUs and GPUs. The final implementation breaks a 79-bit system in about two hours using 80 GPUs and 94-bits system in about 15 hours using 256 GPUs. Extensive experimentation investigates scalability of CPU, GPU and CPU+GPU runs. The discussed results indicate that speed-up is not a good metric for parallel scalability. This paper proposes and evaluates a new metric that is better suited for this task.

The Critical Node Detection Problem (CNDP) is a well-known NP-complete, graph-theoretical problem with many real-world applications in various fields such as social network analysis, supply-chain network analysis, transport engineering, network immunization, and military strategic planning. We present the first parallel algorithms for CNDP solving in general, and for fast, approximated CND on GPU and in the cloud in particular. Finally, we discuss results of our experimental performance analysis of these solutions.

Hashing algorithms are used extensively in information security and digital forensics applications. This paper presents an efficient parallel algorithm hash computation. It's a modification of the SHA-1 algorithm for faster parallel implementation in applications such as the digital signature and data preservation in digital forensics. The algorithm implements recursive hash to break the chain dependencies of the standard hash function. We discuss the theoretical foundation for the work including the collision probability and the performance implications. The algorithm is implemented using the OpenMP API and experiments performed using machines with multicore processors. The results show a performance gain by more than a factor of 3 when running on the 8-core configuration of the machine.

Hashing algorithms are used extensively in information security and digital forensics applications. This paper presents an efficient parallel algorithm hash computation. It's a modification of the SHA-1 algorithm for faster parallel implementation in applications such as the digital signature and data preservation in digital forensics. The algorithm implements recursive hash to break the chain dependencies of the standard hash function. We discuss the theoretical foundation for the work including the collision probability and the performance implications. The algorithm is implemented using the OpenMP API and experiments performed using machines with multicore processors. The results show a performance gain by more than a factor of 3 when running on the 8-core configuration of the machine.
 

Security companies have recently realised that mining massive amounts of security data can help generate actionable intelligence and improve their understanding of Internet attacks. In particular, attack attribution and situational understanding are considered critical aspects to effectively deal with emerging, increasingly sophisticated Internet attacks. This requires highly scalable analysis tools to help analysts classify, correlate and prioritise security events, depending on their likely impact and threat level. However, this security data mining process typically involves a considerable amount of features interacting in a non-obvious way, which makes it inherently complex. To deal with this challenge, we introduce MR-TRIAGE, a set of distributed algorithms built on MapReduce that can perform scalable multi-criteria data clustering on large security data sets and identify complex relationships hidden in massive datasets. The MR-TRIAGE workflow is made of a scalable data summarisation, followed by scalable graph clustering algorithms in which we integrate multi-criteria evaluation techniques. Theoretical computational complexity of the proposed parallel algorithms are discussed and analysed. The experimental results demonstrate that the algorithms can scale well and efficiently process large security datasets on commodity hardware. Our approach can effectively cluster any type of security events (e.g., spam emails, spear-phishing attacks, etc) that are sharing at least some commonalities among a number of predefined features.