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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

In the real word, temporal data can be found in many applications, and it is rapidly increasing nowadays. It is urgently important and challenging to manage and operate big temporal data efficiently and effectively, due to the large volume of big temporal data and the real-time response requirement. Processing big temporal data using a distributed system is a desired choice, since a single-machine based system usually has the limited computing ability. Nevertheless, existing distributed systems or methods either are disk-based solutions, or cannot support native queries, which may not well meet the demands of low latency and high throughput. To attack these issues, this article suggests a new approach to handle big temporal data. Our approach is an In-memory based Two-level Index Solution in Spark, dubbed as ITISS. The proposed framework of our solution is easily understood and implemented, but without loss of effectiveness and efficiency. Based on the proposed framework, this article develops targeted algorithms for handling time travel, temporal aggregation, and temporal join queries, respectively. We have implemented our framework in Apache Spark, extended the Apache Spark SQL to support declarative SQL interface that enables users to perform temporal queries with a few lines of SQL statements, and conducted extensive experiments to verify the performance of our solution. The experimental results, based on both real and synthetic datasets, consistently demonstrate that our proposed solution is efficient and competitive for processing big temporal data.

ITISS: an efficient framework for querying big temporal data

DRAM faults are major hardware sources of cloud node unavailability. To enable early preventive actions and mitigate DRAM fault impacts, prior studies focus on predicting DRAM uncorrectable errors (UEs) that typically cause immediate node unavailability. In our cloud with over half a million nodes, we firstly observe that the correctable error storm (numerous CEs occur in a short period) dominates 56% DRAM-caused node unavailability (DCNU). Therefore, we propose to predict DCNU that takes account into both UEs and CE storms. Observing that DCNUs have strong relevance to temporal statistics and spatial patterns of CEs, we design novel spatio-temporal features to train the prediction model. Considering the model’s real effects cannot be evaluated by traditional metrics like F1-score, we propose a new metric NURR to quantify the node unavailability reduction and tune model hyperparameters with NURR. Our approach achieves over 40% better NURR than existing methods on historical data and runs stably in the production environment.

Predicting DRAM-Caused Node Unavailability in Hyper-Scale Clouds

Probabilistic range query (PRQ) over uncertain moving objects has attracted much attentions in recent years. Most of existing works focus on the PRQ for objects moving freely in two-dimensional (2D) space. In contrast, this paper studies the PRQ over objects moving {in a constrained 2D space} where objects are forbidden to be located in some specific areas. We dub it the constrained space probabilistic range query (CSPRQ). We analyse its unique properties and show that to process the CSPRQ using a straightforward solution is infeasible. The key idea of our solution is to use a strategy called \textit{pre-approximation} that can reduce the initial problem to a highly simplified version, implying that it makes the rest of steps easy to tackle. In particular, this strategy itself is pretty simple and easy to implement. Furthermore, motivated by the cost analysis, we further optimize our solution. The optimizations are mainly based on two insights: (\romannumeral 1) the number of \textit{effective subdivision}s is no more than 1; and (\romannumeral 2) an entity with the larger \textit{span} is more likely to subdivide a single region. We demonstrate the effectiveness and efficiency of our proposed approaches through extensive experiments under various experimental settings, and highlight an extra finding, i.e., the precomputation based approach suffers a non-trivial preprocessing time, which offers an important indication sign for the future research.

Probabilistic Range Query over Uncertain Moving Objects in Constrained Two-dimensional Space

Reachable region query and its applications

With the growth of dirty data, data cleaning turns into a crux of data analysis. In this paper, we propose a novel hybrid data cleaning framework, 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 Markov logic network (MLN) and then builds a two-layer MLN index 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 then, it eliminates the conflict values among different data versions using a novel concept of fusion score. Considerable experimental results on both real and synthetic scenarios demonstrate the effectiveness of MLNClean.

Bin Yao

Papers

ITISS: an efficient framework for querying big temporal data

Predicting DRAM-Caused Node Unavailability in Hyper-Scale Clouds

Probabilistic Range Query over Uncertain Moving Objects in Constrained Two-dimensional Space

Reachable region query and its applications

A Hybrid Data Cleaning Framework Using Markov Logic Networks (Extended Abstract)