Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 105 for “"Hashing"”.
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Beyond locality-sensitive hashing
… structure that bypasses a locality-sensitive hashing lower bound proved by O'Donnell, Wu and Zhou (ICS 2011). By known reductions we obtain a data structure for the Hamming space and ... which is the first improvement over the result of Indyk and Motwani (STOC 1998).
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Perfect hashing and related problems
… a hash table with no collisions. Thus, perfect hashing is a special case of hashing, in which a find operation takes 𝑂(1) time in the worst case, and an insert or a delete operation takes 𝑂(1) time in the average case and 𝑂(𝑛) time in the worst case. This thesis addresses the following issues: …
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Accelerated clustering through locality-sensitive hashing
We obtain improved running times for two algorithms for clustering data: the expectation-maximization (EM) algorithm and Lloyd's algorithm. The EM algorithm is a heuristic for finding a mixture of k normal distributions in Rd that maximizes the probability of drawing n given data points. Lloyd's …
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Similarity hashing of malware on IoT devices
A security threat to enterprise networks is the malware that exists on IoT devices which is rarely controlled at the same level that are observed for conventional computing devices. More specifically, IoT devices are poorly monitored for malware. Through self-modification, this malware attempts to …
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Distributed web caching system with consistent hashing
Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
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Learning compact hashing codes for large-scale similarity search
… objects should have similar representations. Hashing algorithms, which encode objects into compact binary codes to preserve similarity, have demonstrated promising results in addressing these challenges. This dissertation studies the problem of learning compact hashing codes for large-scale …
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Hashing hardware : identifying hardware during boot-time system verification
Modern systems measure the software loaded at boot-time to ensure the machine starts in a trusted state. Such measurements, however, do not include any information about the underlying hardware of the machine. Recent DRAM-based attacks and the growing complexity of the supply chain attest to the …
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Application of minimal perfect hashing in main memory indexing
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
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Physiological time series retrieval and prediction with locality-sensitive hashing
… goal, we propose to apply locality-sensitive hashing (LSH), which supports a very fast approximate nearest neighbor search in high dimensions. We empirically demonstrate that LSH based retrieval and prediction methods vastly speed up querying time while sacrificing only a trivial amount of …
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Anonymous Multicast Communication for Wireless Networks Using Layered Encryption and Hashing
… solution is based on the layered encryption and hashing for multicasting the data to multiple receivers without compromising the identity of the receiver. We present layered encryption and hashing (LEH) approach for this problem. Our approach uses hash ID to identify as well as randomize a node. …
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Consistent hashing and random trees : algorithms for caching in distributed networks
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
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Resource-Efficient Machine Learning via Count-Sketches and Locality-Sensitive Hashing (LSH)
… thesis, we explore how to use locality-sensitive hashing (LSH) and the count-sketch data structure for addressing the computational and memory challenges in four distinct areas. (1) The LSH Sampling algorithm uses the LSH data structure as an adaptive sampler. We demonstrate this LSH Sampling …
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Evaluating parameter optimization in locality-sensitive hashing for high-dimensional physiological waveforms
… inform parameter choice for locality-sensitive hashing methods used towards identifying similarity in physiological waveform time-series data. The goal is to achieve increased probability of successful patient outcomes in emergency rooms by tackling the problem of efficient information retrieval …
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Relationships between functionality, security, and privacy for multiparty computation, hashing, and encryption
… of privacy. Second, we study Property Preserving Hashing, which can be thought of as an extension of collision-resistant hashing where we add functionality. Finally, we explore Fine-Grained Cryptography, and develop a public key cryptosystem. In this model of cryptography, security takes on a much …
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Learning compact hashing codes with complex objectives from multiple sources for large scale similarity search
… scale similarity search becomes more important.^ Hashing methods have become popular for large scale similarity search due to their computational and memory efficiency. These hashing methods design compact binary codes to represent data examples so that similar examples are mapped into similar …
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Genomically encoded logic gates and cell-cell communication devices for the implementation of a cryptographic hashing algorithm in living cells
… used to implement a recoded version of the MD5 hashing funcon, a historically widely used cryptography algorithm. The circuit requires 110 logic gates paroned across 65 E. coli strains, requiring a total of 0.66 Mb of recombinant DNA introduced onto their genomes with the most complex strain …
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Generative models meet similarity search: efficient, heuristic-free and robust retrieval
… methods, especially the Learning-to-hash or Hashing methods, provide principled approaches that balance the trade-offs between the quality of the guesses and the computational cost for web-scale databases. In this era of data explosion, it is crucial for the hashing methods to be both …
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Large-Scale Machine Learning for Classification and Search
… semi-supervised learning and unsupervised hashing algorithms. Our unique contributions on the graph-related topics include: 1. Large Graph Construction: Conventional neighborhood graphs such as kNN graphs require a quadratic time complexity, which is inadequate for large-scale applications …
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An analysis of SIFT object recognition with an emphasis on landmark detection
… SIFT object recognition and Locality-Sensitive Hashing to quickly and accurately detect landmarks with accuracies ranging from 85-95%. I have also compared PCA-SIFT, a newly developed feature descriptor, to SIFT, and have found that SIFT outperforms it only particular data set. In addition, I …
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