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Keyword-based search over encrypted outsourced data has become an important tool in the current cloud computing scenario. The majority of the existing techniques are focusing on multi-keyword exact match or single keyword fuzzy search. However, those existing techniques find less practical significance in real-world applications compared with the multi-keyword fuzzy search technique over encrypted data. The first attempt to construct such a multi-keyword fuzzy search scheme was reported by Wang et al. , who used locality-sensitive hashing functions and Bloom filtering to meet the goal of multi-keyword fuzzy search. Nevertheless, Wang’s scheme was only effective for a one letter mistake in keyword but was not effective for other common spelling mistakes. Moreover, Wang’s scheme was vulnerable to server out-of-order problems during the ranking process and did not consider the keyword weight. In this paper, based on Wang et al. ’s scheme, we propose an efficient multi-keyword fuzzy ranked search scheme based on Wang et al. ’s scheme that is able to address the aforementioned problems. First, we develop a new method of keyword transformation based on the uni-gram, which will simultaneously improve the accuracy and creates the ability to handle other spelling mistakes. In addition, keywords with the same root can be queried using the stemming algorithm. Furthermore, we consider the keyword weight when selecting an adequate matching file set. Experiments using real-world data show that our scheme is practically efficient and achieve high accuracy.

Toward Efficient Multi-Keyword Fuzzy Search Over Encrypted Outsourced Data With Accuracy Improvement

Mathematical programming

With the increasing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaption methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.

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Privacy-preserving multi-keyword text search in the cloud supporting similarity-based ranking

Spatial Crowdsourcing (SC) is a transformative platform that engages individuals, groups and communities in the act of collecting, analyzing, and disseminating environmental, social and other spatio-temporal information. The objective of SC is to outsource a set of spatio-temporal tasks to a set of workers, i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations of interest. However, current solutions require the workers, who in many cases are simply volunteering for a cause, to disclose their locations to untrustworthy entities. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We argue that existing location privacy techniques are not sufficient for SC, and we propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We investigate analytical models and task assignment strategies that balance multiple crucial aspects of SC functionality, such as task completion rate, worker travel distance and system overhead. Extensive experimental results on real-world datasets show that the proposed technique protects workers' location privacy without incurring significant performance metrics penalties.

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A framework for protecting worker location privacy in spatial crowdsourcing

Nearest neighbor search is a fundamental computational tool and has wide applications. In past decades, many datastructures have been developed to speed up this operation. In this paper, we propose a novel hierarchical datastructure for nearest neighbor search in moderately high dimension. Our proposed method maintains good run time guarantees, and it outperforms several state-of-the-art methods in practice.

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An Improved Hierarchical Datastructure for Nearest Neighbor Search

In this paper, we investigate the problem of Boolean operation on circular-arc polygons. We present a concise and efficient method, in which the concept of related edges is introduced. Given two circular-arc polygons with M and N edges, respectively, the proposed method only takes O((l+k)∗ log l+M +N) time, where k is the number of intersection points, l is the number of related edges. The proposed method can be easily extended to deal with Boolean operation on traditional polygons.

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An Efficient Algorithm for Boolean Operation on Circular-arc Polygons

A spatial data-based method of safety range query is characterized in that when a client acquires an ID (identifier) from a server, the ID is decrypted and is re-encrypted when the client returns data of the server. The method has the advantages that query efficiency is ensured, data encryption is implemented, a data access mode is hidden and protected, and the risk of information leakage is greatly decreased.

Spatial data-based method of safety range query

In this study, we present a novel tree based index scheme for efficient indexing and serving large datasets in the cloud. It incorporates and extends the functionality of Hadoop to create a fully parallel index system. Our new scheme can be summarized as follows. First, we leverage the MapReduce framework to create an index, then publish the index meta information and write it into a meta table. Second, we use the meta information to help the system adopting an efficient method to handle a given query. Finally, we optimize the system by using cache mechanism. We conduct extensive experiments on the Hadoop cluster to demonstrate the scalability, availability and efficiency of the proposed index framework.

A Generic Tree-Like Index Framework in the Cloud

This paper studies the constrained-space probabilistic threshold range query (CSPTRQ) for moving objects. We differentiate two kinds of CSPTRQs: implicit and explicit ones. Specifically, for each moving object $o$, we assume $o$ cannot be located in some specific areas, we model its location as a closed region, $u$, together with a probability density function, and model a query range, $R$, as an arbitrary polygon. An implicit CSPTRQ can be reduced to a search (over all the $u$) that returns a set of objects, which have probabilities higher than a probability threshold $p_t$ to be located in $R$, where $0\leq p_t\leq 1$. In contrast, an explicit CSPTRQ returns a set of tuples in form of ($o$, $p$) such that $p\geq p_t$, where $p$ is the probability of $o$ being located in $R$. A straightforward adaptation of existing method is inefficient due to its weak pruning/validating capability. In order to efficiently process such queries, we propose targeted solutions, in which three main ideas are incorporated: (1) swapping the order of geometric operations based on the computation duality; (2) pruning unrelated objects in the early stages using the location unreachability; and (3) computing the probability using the multi-step mechanism. Extensive experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.

Bin Yao

Papers

An Improved Hierarchical Datastructure for Nearest Neighbor Search

An Efficient Algorithm for Boolean Operation on Circular-arc Polygons

Spatial data-based method of safety range query

A Generic Tree-Like Index Framework in the Cloud

Explicit and Implicit Constrained-Space Probabilistic Threshold Range Queries for Moving Objects