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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

With the increase of dirty data, data cleaning turns into a crux of data analysis. The accuracy limitation of the existing integrity constraints-based cleaning approaches results from insufficient rules. In this paper, we present a novel hybrid data cleaning framework on top of Markov logic networks (MLNs), termed as MLNClean, which is capable of learning instantiated rules to supplement the insufficient integrity constraints. MLNClean consists of two steps, i.e., pre-processing and two-stage data cleaning. In the pre-processing step, MLNClean first infers a set of probable instantiated rules according to MLNs and then builds a two-layer MLN index structure to generate multiple data versions and facilitate the cleaning process. In the two-stage data cleaning step, MLNClean first presents a concept of reliability score to clean errors within each data version separately, and afterward eliminates the conflict values among different data version using a novel concept of fusion score. Considerable experimental results on both real and synthetic scenarios demonstrate the effectiveness of MLNClean in practice.

A Hybrid Data Cleaning Framework Using Markov Logic Networks

Multiple query optimization (MQO) in the cloud has become a promising research direction due to the popularity of cloud computing, which runs massive data analysis queries (jobs) routinely. These CPU/IO intensive analysis queries are complex and time-consuming but share common components. It is challenging to detect, share and reuse the common components among thousands of SQL-like queries. Previous solutions to MQO, heuristic or genetic based, are not appropriate for the large growing query set situation. In this paper, we develop a sharing system called LSShare using our proposed Lineage-Signature approach. By LSShare, we can efficiently solve the MQO problem in a recurring query set situation in the cloud. Our system has been prototyped in a distributed system built for massive data analysis based on Alibaba's cloud computing platform ( http://www.alibaba.com/ ). Experimental results on real data sets demonstrate the efficiency and effectiveness of the proposed approach.

LSShare: an efficient multiple query optimization system in the cloud

With the popularity of mobile devices and the development of geo-positioning technology, location-based services (LBS) attract much attention and top-k spatial keyword queries become increasingly complex.It is common to see that clients issue a query to find a restaurant serving pizza and steak, low in price and noise level particularly.However, most of prior works focused only on the spatial keyword while ignoring these independent numerical attributes.

QDR-Tree: An Efficient Index Scheme for Complex Spatial Keyword Query

Aggregate nearest neighbor ( Ann ) query in both the euclidean space and road networks has been extensively studied, and the flexible aggregate nearest neighbor ( Fann ) problem further generalizes Ann by introducing an extra flexibility parameter $\phi$ ϕ that ranges in $(0, 1]$ ( 0 , 1 ] . In this article, we focus on Fann on road networks, denoted as Fann $_\mathcal {R}$ R , and its keyword-aware variant, denoted as KFann $_\mathcal {R}$ R . To solve these problems, we propose a series of universal (i.e., suitable for both max and sum ) algorithms, including a Dijkstra-based algorithm that enumerates $P$ P instead of $\phi |Q|$ ϕ | Q | -combinations of $Q$ Q , a queue-based approach that processes data points from-near-to-far, and a framework that combines incremental euclidean restriction (IER) and $k$ k NN. We also propose a specific exact solution to max - Fann $_\mathcal {R}$ R and a constant-factor ratio approximate solution to sum - Fann $_\mathcal {R}$ R . These specific algorithms are easy to implement and can achieve excellent performance in some scenarios. Besides, we further extend this problem to top- $k$ k and multiple Fann $_\mathcal {R}$ R (resp., KFann $_\mathcal {R}$ R ) queries. We conduct a comprehensive experimental evaluation for the proposed algorithms on real datasets to demonstrate their superior efficiency and high quality.

Flexible Aggregate Nearest Neighbor Queries and its Keyword-Aware Variant on Road Networks

Music playlist recommendation is an important component in modern music streaming services, which is used for improving user experience by regularly pushing personalized music playlists based on users’ preferences. In this paper, we propose a novel music playlist recommendation problem, namely Personalized Music Playlist Recommendation (PMPR), which aims to provide a suitable playlist for a user by taking into account her long/short-term preferences and music contextual data. We propose a data-driven framework, which is comprised of two phases: user/music feature extraction and music playlist recommendation. In the first phase, we adopt a matrix factorization technique to obtain long-term features of users and songs, and utilize the Paragraph Vector (PV) approach, an advanced natural language processing technique, to capture music context features, which are the basis of the subsequent music playlist recommendation. In the second phase, we design two Attention-based Long Short-Term Memory (AB-LSTM) models, i.e., typical AB-LSTM model and Improved AB-LSTM (IAB-LSTM) model, to achieve the suitable personalized playlist recommendation. Finally, we conduct extensive experiments using a real-world dataset, verifying the practicability of our proposed methods.

Bin Yao

Papers

A Hybrid Data Cleaning Framework Using Markov Logic Networks

LSShare: an efficient multiple query optimization system in the cloud

QDR-Tree: An Efficient Index Scheme for Complex Spatial Keyword Query

Flexible Aggregate Nearest Neighbor Queries and its Keyword-Aware Variant on Road Networks

Music Playlist Recommendation with Long Short-Term Memory