Locality Sensitive Hardware Signature Variants for Hardware Transactional Memory

Lock based techniques have its own limitations like priority inversion, convoying, and deadlock. Lock free techniques overcome those mentioned limitations. Transactional memory (TM) is one leading lock free technique used in recent multi core processors like Intel Haswell and IBM BlueGene/Q. TM has to do data versioning and conflict detection. For conflict detection probabilistic data structure called Bloom Filters are used. Bloom filter based hardware signatures are used in TM. In TM shared memory conflicts like RAW, WAR, and WAW hazards are handled by Bloom Filter (BF). Hardware signatures store memory addresses in hashed form on Bloom filters. Bloom filters are easy to use, performance efficient data structures lead to false positive but never support false negative. Locality sensitive hardware signatures reduce filter occupancy by sharing bits for the contiguous memory addresses, in turn reduces the false positive rate. This paper implements existing H3 – HS and LS – HS proposed by Ricardo Quislant et al. [13]. Also this paper proposes RS – HS, CS – HS, and RO – HS. RO – HS equally spreads addresses among bloom filters thereby reduces filter occupancy. In turn reduced filter occupancy leads to better False Positive Rate.

Privacy-Enhanced Robust Image Hashing with Bloom Filters

Robust image hashes are used to detect known illegal images, even after image processing. This is, for example, interesting for a forensic investigation, or for a company to protect their employees and customers by filtering content. The disadvantage of robust hashes is that they leak structural information of the pictures, which can lead to privacy issues. Our scientific contribution is to extend a robust image hash with privacy protection. We thus introduce and discuss such a privacy-preserving concept. The approach uses a probabilistic data structure -- known as Bloom filter -- to store robust image hashes. Bloom filter store elements by mapping hashes of each element to an internal data structure. We choose a cryptographic hash function to one-way encrypt and store elements. The privacy of the inserted elements is thus protected. We evaluate our implementation, and compare it to its underlying robust image hashing algorithm. Thereby, we show the cost with respect to error rates for introducing a privacy protection into robust hashing. Finally, we discuss our approach's results and usability, and suggest possible future improvements.

Moore Data Clustering Based Bloom Hash Storage for Dimensionality Reduction of Big Data Analytics

Big data contains massive amounts of information’s that are difficult to manage, acquire, store and analyses. The clustering of data is a demanding issue in the field of big data analytics. The existing techniques developed for clustering does not provide efficient performance and also time complexity of clustering was higher. Further, minimizing dimensionality of big data was not addressed effectively. In order to overcome these limitations, a Moore Data Clustering based Bloom Hash Storage (MDC-BHS) Technique is proposed. The MDC-BHS Technique is designed with aim of reducing the dimensionality of big data with lesser time through clustering. The MDC-BHS Technique used Moore Data Clustering (MDC) Model in order to group the data in big dataset with minimum time consumption. After performing clustering process, the MDC-BHS Technique employed Bloom Hash Storage (BHS) Model in order to store clustered data with minimum space complexity. The BHS Model is a space-efficient probabilistic data structure which utilized hashing function to create hash value for clustered data. Therefore, proposed MDC-BHS Technique significantly reduces the dimensionality of larger dataset. The experimental evaluation of MDC-BHS technique is carried out on weather data with factors such as clustering time and clustering accuracy and space complexity with respect to number of data. The experimental results demonstrate that MDC-BHS Technique is able to improve the clustering accuracy and also minimizes the space complexity when compared to state-of-the-art works

Creating a Concurrent Overflowing Bloom Filter

Bloom filters are an efficient probabilistic data structure used to verify membership of an element inside of a set. There is diminishing marginal value for inserting each additional element into a Bloom filter, and so steps must be taken to maintain scalability. One such option is to create a secondary hash set for a particular hash set in a Bloom filter that has become full, known as an overflow area. At this time, there are no implementations of a Bloom filter that implement this overflow system while maintaining concurrency. In this paper, we demonstrate the creation of a concurrent overflow system for Bloom filters. We use the base Bloom filter presented in recent literature and replace their method of dynamically resizing the Bloom filters with our overflow table implementation, as outlined in one of their suggested areas for future exploration. We then compare the results of our Bloom filter with those from the previously mentioned implementation as well as a standard Bloom filter.

Creating a Concurrent Overflowing Bloom Filter

Bloom filters are an efficient probabilistic data structure used to verify membership of an element inside of a set. There is diminishing marginal value for inserting each additional element into a Bloom filter, and so steps must be taken to maintain scalability. One such option is to create a secondary hash set for a particular hash set in a Bloom filter that has become full, known as an overflow area. At this time, there are no implementations of a Bloom filter that implement this overflow system while maintaining concurrency. In this paper, we demonstrate the creation of a concurrent overflow system for Bloom filters. We use the base Bloom filter presented in recent literature and replace their method of dynamically resizing the Bloom filters with our overflow table implementation, as outlined in one of their suggested areas for future exploration. We then compare the results of our Bloom filter with those from the previously mentioned implementation as well as a standard Bloom filter.

Provably Secure Private Set Intersection With Constant Communication Complexity

Electronic information is increasingly shared among unreliable entities. In this context, one interesting problem involves two parties that secretly want to determine an intersection of their respective private data sets while none of them wish to disclose the whole set to the other. One can adopt a Private Set Intersection (PSI) protocol to address this problem preserving the associated security and privacy issues. In this article, the authors present the first PSI protocol that incurs constant (p(k)) communication complexity with linear computation overhead and is fast even for the case of large input sets, where p(k) is a polynomial in security parameter k. Security of this scheme is proven in the standard model against semi-honest entities. The authors combine somewhere statistically binding (SSB) hash function with indistinguishability obfuscation (iO) and space-efficient probabilistic data structure Bloom filter to design the scheme.