Locality-sensitive hashing scheme based on dynamic collision counting

Gan, Junhao; Feng, Jiali; Fang, Qiong; Ng, Wilfred

doi:10.1145/2213836.2213898

Cited by 174 publications

(262 citation statements)

References 10 publications

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“…The number of messages is an important indication of algorithm efficiency. Query accuracy [6] is used to measure the quality of the results. Specifically, o * is the actual result and o are the returned results, query accuracy is DistST(q,o)…”

Section: Methodsmentioning

confidence: 99%

“…Given that spatial location based similarity is defined based on Euclidean distance and textual similarity is defined based on Jaccard distance, [6] and [7] describe the construction of an LSH family for Euclidean distance and the construction of an LSH family for Jaccard distance respectively.…”

Section: Overviewmentioning

confidence: 99%

“…Because the traditional (R, c)-NN problem [6,8] just adapts to objects with single data type. we extend the (R, c)-NN problem to (D, R, c)-NN for spatiotextual objects, which return an object o ∈ B(q, cD, cR) if there exists an object o * ∈ B(q, D, R).…”

Section: Overviewmentioning

confidence: 99%

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Hybrid-LSH for Spatio-Textual Similarity Queries

Zhu

Shen

Liu

et al. 2015

Web Technologies and Applications

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Abstract. Locality Sensitive Hashing (LSH) is a popular method for high dimensional indexing and search over large datasets. However, little efforts have put forward to utilizing LSH in mobile applications for processing spatio-textual similarity queries, such as find nearby shopping centers that have a top ranked hair salon. In this paper, we present hybrid-LSH, a new LSH method for indexing data objects according to both their spatial location and their keyword similarity. Our hybrid-LSH approach has two salient features: First our hybrid-LSH carefully combines the spatial location based LSH and textual similarity based LSH to ensure the correctness of the spatial and textual similarity based NN queries. Second, we present an adaptive query-processing model to address the fixed range problem of traditional LSH and to handle queries with varying ranges effectively. Extensive experiments conducted on both synthetic and real datasets validate the efficiency of our hybrid LSH method.

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Section: Methodsmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

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Hybrid-LSH for Spatio-Textual Similarity Queries

Zhu

Shen

Liu

et al. 2015

Web Technologies and Applications

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“…According to Definition 1, LSH ensures the property aforementioned. In order to amplify the gap between P 1 and P 2 , the state-of-theart LSH algorithm Gan et al (2012) constructs a compound LSH function G = (h 1 , . .…”

Section: Locality-sensitive Hashingmentioning

confidence: 99%

“…It has been used to perform ANN search in high-dimensional space. Based on the basic LSH techniques (Datar et al 2004;Indyk and Motwani 1998), several solutions have been proposed in literature, such as LSBforest (Tao et al 2010), C2LSH (Gan et al 2012). Recently, Liu et al (2014) proposed a novel index structure, called SortingKeys-LSH (SK-LSH), which can effectively reduce the number of page accesses while ensuring the quality of search result.…”

Section: Introductionmentioning

confidence: 99%

Enabling efficient approximate nearest neighbor search for outsourced database in cloud computing

et al. 2015

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Approximate nearest neighbor (ANN) search in high-dimensional space has been studied extensively in recent years. However, it supports only ANN search over plaintext in traditional locality-sensitive hashing (LSH). How to perform ANN search over encrypted data becomes a new challenging task. In this paper, we make an attempt to formally address the problem. We propose a new secure and efficient ANN search scheme over encrypted data based on SortingKeys-LSH (SK-LSH) and mutable order-preserving encryption (mOPE). In our construction, a secure index is generated by incorporating SK-LSH with mOPE, which can simultaneously achieve efficient ANN search and ensure data confidentiality. Furthermore, the proposed solution can achieve efficient range query on encrypted data. Security analysis demonstrates that our construction can achieve the desired security properties.

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LSR‐forest: An locality sensitive hashing‐based approximate k‐nearest neighbor query algorithm on high‐dimensional uncertain data

Wang

Qian

Yang

et al. 2020

Concurrency and Computation

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Summary Uncertain data is widely used in many practical applications, such as data cleaning, location‐based services, privacy protection, and so on. With the development of technology, data has a tendency to high‐dimensionality. The most common indexes for nearest neighbor search on uncertain data are the R‐Tree and the KD‐Tree. These indexes will inevitably bring about “curse of dimension.” Focus on this problem, article proposes a new hash algorithm, called the LSR‐forest, which based on locality sensitive hashing and R‐Tree, to solve the high‐dimensional uncertain data approximate neighbor search problem. The LSR‐forest can hash similar high‐dimensional uncertain data into a same bucket with a high probability, and then constructs multiple R‐Tree‐based indexes for hashed buckets. When querying, it is possible to judge neighbors by checking the data in the hypercube which the query point is in. One can also adjust the query range automatically by different parameter of k. Many experiments on different datasets are presented in this article. The results show that LSR‐forest has better effectiveness and efficiency than R‐Tree on high‐dimensional datasets.

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Locality-sensitive hashing scheme based on dynamic collision counting

Cited by 174 publications

References 10 publications

Hybrid-LSH for Spatio-Textual Similarity Queries

Hybrid-LSH for Spatio-Textual Similarity Queries

Enabling efficient approximate nearest neighbor search for outsourced database in cloud computing

LSR‐forest: An locality sensitive hashing‐based approximate k‐nearest neighbor query algorithm on high‐dimensional uncertain data

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