In the modern digital landscape, integrating geographic locations and textual descriptions within a geo-textual dataset enhances location-based services (LBS) via spatial keyword queries, as these queries combine spatial and textual information to deliver more precise and personalized results. Additionally, the advent of cloud computing allows data owners to outsource data management and services to the cloud, boosting scalability but introducing efficiency challenges due to complex encryption. Although many schemes have been proposed for spatial keyword queries on encrypted geo-textual data, none supports matching a query keyword set with the keyword sets of multiple objects, a common query type in LBS. Imagine a user seeking to rent a house close to his/her workplace, with easy access to conveniences like supermarkets. By using nearby-fit spatial keyword queries, we can match the desired house with a house-type target object and its nearby amenities, offering more practical and flexible recommendations than traditional spatial keyword queries. Hence, in this article, we introduce an efficient and privacy-preserving scheme called the privacy-preserving weighted nearby-fit spatial keyword (PWNSK) query scheme. First, we design a target-oriented spatial keyword (TOSK) tree for data organization and a TOSK tree-based weighted nearby-fit spatial keyword (WNSK) query algorithm for efficient pruning by simultaneously utilizing locations, keywords, and distances from nearby objects to target objects. For privacy, we develop several protocols, including one for polynomial coefficient re-encoding, based on polynomial coefficient encoding and fully homomorphic encryption. Building on these protocols, we introduce our PWNSK scheme. A thorough security analysis confirms its robustness, while extensive experiments also showcase its effectiveness.

The Internet of Things continues to develop at a rapid pace, enabling Internet Service Providers (ISPs) to accumulate voluminous data. To fully utilize these data, ISPs want to provide range query services to users. However, data are often collected with missing attributes (i.e., incomplete dataset), which can lead to inaccurate data analysis and decision-making. In addition, ISPs are increasingly outsourcing data and range query services to the cloud with the massive computing power. However, in the absence of full trust in the cloud, data must be encrypted prior to outsourcing, inevitably making query services more difficult on incomplete datasets. Existing schemes either fail to handle datasets with missing attributes or overlook privacy concerns. To address this problem, we propose a scheme for privacy-preserving multi-dimensional range queries over incomplete datasets. Firstly, we use the mapping function to complete the records with missing attributes and add bitstrings to identify the corresponding records. Secondly, a Bitstring-augmented R-tree (BR-tree) is constructed for the complemented multi-dimensional data. Then, we design a privacy-preserving multi-dimensional range intersection determination protocol and a privacy-preserving bitstring comparison protocol to achieve a secure range query using a symmetric homomorphic encryption technique. Then, on the basis of BR-tree and protocols, a scheme for privacy-preserving multi-dimensional range queries on incomplete datasets is proposed. At last, we conducted security analysis and performance evaluation. The results indicate that the scheme can provide safety and effective range queries over attributes with missing data.

As the mobile Internet grows rapidly, location-based services (LBSs) are widely applied in the tourism and transportation fields. To fully mine the data collected, location service providers (LSPs) intend to offer various query services to users, which include eclipse query. The eclipse query can generalize nearest neighbor queries and skyline queries and allow users to set more rough and customizable preference ranges. In addition, with the boom of cloud computing, more and more LSPs hope to leverage the cloud to offer better query services. Given that the data could potentially contain confidential information, the data are required to be encrypted prior to outsourcing them. Therefore, eclipse queries need to be executed on the ciphertext. Although several schemes for eclipse queries have been proposed in existing works, they have little focus on privacy issues. To address this issue, we propose an efficient and privacy-preserving scheme for eclipse queries (EPEQ) in this paper. First, we develop a MinValue tree to construct an index for the dataset. Then, by utilizing the MinValue tree and a symmetric homomorphic encryption technique, we design a secure minimum value comparison protocol to obtain a skyline data and a secure undominated data acquisition protocol to obtain the data not skyline dominated by the skyline data. After that, we present our scheme. We analyze the security of the EPEQ scheme and perform experimental evaluations, demonstrating the security and efficiency of our EPEQ scheme.

With the popularity of location-based social networks (LBSNs), locations and social relationships have been considered to be important factors in point-of-interest (POI) recommendation services. The boom of cloud computing has driven data owners to outsource the LBSN data and the POI recommendation services to the cloud with powerful computing and storage capabilities. However, as the data usually contains sensitive information, it should be encrypted before being out-sourced, and consequently, the POI recommendation has to be processed over encrypted data. Although several privacy-preserving LBSNs-based POI recommendation schemes have been proposed, they are either inapplicable to the outsourcing scenario or have issues with the recommendation accuracy. Aiming at addressing these issues, in this paper, we propose an efficient and privacy-preserving LBSNs-based POI recommendation scheme (PLPR). Specifically, we first index the users' social relationships with Bloom filters and then organize the user resident location and social relationship dataset into a Vantage Point (VP) tree. Then, we design an efficient LSBNs-based POI recommendation algorithm based on the VP tree. After that, we design a privacy-preserving range determination protocol (PRD) and a privacy-preserving neighbor determination protocol (PND) to respectively protect the privacy of locations and social relationships in the designed algorithm and propose our PLPR scheme. Security analysis shows that our scheme is privacy-preserving, and performance evaluation demonstrates that our scheme is also efficient.

The paper explores the issue of multiple-user fairness of intelligent reflecting surface (IRS)-assisted physical layer security (PLS) in the digital twin (DT)-enabler power grid. Previous research works have focused on achieving secrecy rate fairness through beamforming or phase shift optimization. However, in the DT-enabler power grid, the secrecy rate is not available as the instantaneous channel state information (CSI) of the passive eavesdropper is unknown. To address these challenges, we apply an expression for secrecy outage probability, measured based on the statistical CSI of the eavesdropper for the scenario where multiple DT users are present. Using zero-forcing (ZF) precoding at the transmitter, we formulate the problem of achieving fairness in secrecy outage probability, and then solve it by optimizing the phase shift matrices. Simulation results demonstrate that the proposed methods can achieve higher fairness among users in comparison to existing IRS-assisted PLS schemes.

The Internet of Things (IoT) boom has enabled Internet Service Providers (ISPs) to collect an enormous amount of high-dimensional data. Performing range queries on such data can effectively reuse them to help ISPs offer better services. Owing to the low cost and high resource utilization of cloud computing, an increasing number of ISPs are inclined to outsource data and services to it. However, as the cloud is not fully trusted, data need to be encrypted before being outsourced, which inevitably hinders many query services, e.g., range queries. Various schemes were proposed for privacy-preserving range queries, yet they struggled to extend to high-dimensional scenarios and did not support dimension selection. Aiming at this challenge, in this article, we propose an efficient and privacy-preserving high-dimensional range query scheme (PHRQ) based on an iMinMax tree while supporting dimension selection. Specifically, we first build an iMinMax tree for high-dimensional data and utilize a symmetric homomorphic encryption technique to design a suite of privacy-preserving protocols to achieve secure high-dimensional range queries. Then, we design a sub-dimensional range determination protocol to support dimension selection. Further, based on the iMinMax tree and our privacy-preserving protocols, we propose our PHRQ scheme. Finally, security analysis shows that our scheme is privacy-preserving, and performance evaluation demonstrates that our scheme is efficient in high-dimensional range query processing.

JavaScript (JS) engines are a core component of a lot of software, such as web browsers, PDF readers and flash players. There has been much research on finding JS engine vulnerabilities. However, due to the fact that a JS engine's input space is infinite and the vulnerability triggering conditions are extremely strict, it is difficult to generate test cases that are able to trigger deep logic errors in fuzzing. This paper aims to explore an approach which incorporates the human experience into fuzzing. We propose a Key-Operation-based Fuzzer (KOP-Fuzzer), to explore the type confusion vulnerabilities in JS engines. Based on human knowledge, we summarize a trigger model and extract key operations for type confusion vulnerabilities in JS engines. We use clustering to extract the key-operation methods from the engine's source code and develop a fuzzing system for key -operation mutation. Our experimental results demonstrate that the KOP-Fuzzer generates valid test cases with 1.5x fewer runtime errors, while also improving the edge coverage (2.082 %) and key-operation coverage (6.452 %), when compared with the state-of-the-art JS engine fuzzers. The KOP-Fuzzer discovered a total of 21 new bugs in ChakraCore and JavaScriptCore, where 16 of them are caused by the engine's incorrect handling of key operations and 5 of them are caused by type confusions.

In this paper, we study the improvement of a storage location strategy through the use of big data technology, including data collection, cluster analysis and association analysis, to improve order picking efficiency. A clustering algorithm is used to categorize the types of goods in orders. Classification is performed based on the turnover of goods, value, sales volume, favorable commodity ratings, whether free shipping is provided and whether cash on delivery is supported. An association algorithm is used to determine the relationships among goods by studying the habits of consumers who buy them. A method for improving the class-based storage strategy is proposed. The picking distance of the improved storage strategy is compared with that of the traditional strategy via simulation experiments. The picking efficiency is shown to be enhanced by the improved strategy.

Summary Data outsourcing is a major component for cloud computing because data owners are able to distribute resources to external services for sharing with users and organizations. A crucial problem for owners is how to secure sensitive information accessed by legitimate users only using the trusted services. We address the problem with access control methods to enforce selective access to outsourced data without involving the owner in authorization. The basic idea is to combine cryptography with authorizations, and data owners assign keys to roles that will enforce access via encryption. A formal access model is designed to analyze the translating an authorization policy into an equivalent encryption policy. The paper also investigates the effect of role hierarchy structure in the authorization process. The role‐based access management methods are implemented withXACMLby usingWSOIdentity Server. The comparisons with other related work are presented. Finally, the future work is introduced.Copyright © 2014 John Wiley & Sons, Ltd.

Outlier detection is an important long-standing research problem in data mining and has enjoyed applications in a wide range of applications in business, engineering, biology and security, etc. However, the traditional outlier detection methods inevitably need to use different parameters for detection such as those used to specify the distance or density cutoff for distinguish outliers from normal data points. Using the trial and error approach, the traditional outlier detection methods are rather tedious in parameter tuning. In this demo proposal, we introduce an innovative outlier detection system, called SODIT, that uses localized thresholding to assist the value specification of the thresholds that reflect closely the local data distribution. In addition, easy-to-use user feedback are employed to further facilitate the determination of optimal parameter values. SODIT is able to make outlier detection much easier to operate and produce more accurate, intuitive and informative results than before.

Virtualization provides many benefits for data intensive workloads including security, performance isolation, and ease of management and configuration. Unfortunately, current VM technology prevents taking advantage of sharing opportunities, resulting in substantial network traffic and application slowdown. Octopus is a new framework for running data intensive applications on virtualized datacenters. Octopus provides efficient file sharing across VMs running on the same physical host and optimizes the placement of VMs in the cluster to maximize sharing opportunities. Our experiments with a suite of bioinformatics and natural language processing applications show that Octopus reduces network transfer by up to 83% and total runtime by up to 55%.

With the increased development of cloud computing, access control policies have become an important issue in the security filed of cloud computing. Semantic web is the extension of current Web which aims at automation, integration and reuse of data among different web applications such as clouding computing. However, Semantic web applications pose some new requirements for security mechanisms especially in the access control models. In this paper, we analyse existing access control methods and present a semantic based access control model which considers semantic relations among different entities in cloud computing environment. We have enriched the research for semantic web technology with role-based access control that is able to be applied in the field of medical information system or e-Healthcare system. This work demonstrates how the semantic web technology provides efficient solutions for the management of complex and distributed data in heterogeneous systems, and it can be used in the medical information systems as well.

SUMMARY With the growing importance of privacy in data access, much research has been done on the privacy protecting technology in the recent years. Developing an access control model and related mechanisms to support a selective access data has become important. The extensible markup language (XML) is rapidly emerging as the new standard language for semi‐structured data representation and exchange on the Internet with more and more information being distributed in XML format. In this paper, we present a comprehensive approach for privacy preserving access control based on the notion of purpose. In our model, purpose information associated with a given data element in an XML document specifies the intended use of the data elements. An important issue addressed in this paper is the granularity of data labeling for data elements in XML documents and tree databases with which purposes can be associated. We address this issue in native XML databases and propose different labeling schemes for XML documents. We also propose an approach to represent purpose information to support access control based on purpose information. Our proposed solution relies on usage access control models as well as the components that are based on the notions of the purpose information used in subjects and objects. Finally, comparisons with related works are analysed. Copyright © 2011 John Wiley & Sons, Ltd.

This paper proposes a framework for privacy preserving access control policies and mechanisms, and describes algorithms for access policy conflicting problems. The mechanism enforces access policy to data containing personally identifiable information. The key component of the framework is purpose involved access control models (PAC) that provide full support for expressing highly complex privacy-related policies, taking into account features like purposes, conditions and obligations. Policy conflicting problems may arise when new access policies are generated that are possible to be conflicted to existing policies. As a result of the policy conflicts, private information cannot be well protected. The structure of access control policy including conditions and obligations is studied. Based on the access policy, authorization models and policy operations are analysed. Finally comparisons to related works EPAL are presented.

Abstract E‐learning defined as learning activities performed over electronic devices is rapidly increasing and has become one of the key e‐commerce applications with a growing commercial market in the near future. Access control and authorization in E‐learning workflow is a vital problem with low costs and no location limits for students. E‐learning organizers prefer to disseminate their message as widely as possible, while at the same time authorized people only are able to access disseminative information. However, in the E‐learning workflow it is a challenge to protect from access, such as reading and updating, the digital message that has been disseminated out from the organizations. This paper aims to substantially provide a foundation for developing appropriate security solutions for organizations' secure dissemination of digital information. An application‐level secure architecture is developed based on the references on both server side and client side. The architecture provides control and tracking capabilities for dissemination and usage of digital information while others provide only tracking capability. The outcomes of this paper can immediately apply to prevent unauthorized dissemination of digital content amongst agents of universities regardless of their intention or possession of the digital information and will contribute to higher security in E‐learning. Copyright © 2011 John Wiley & Sons, Ltd.

With the growing importance of privacy in data access, much research has been done on the privacy protecting technology in recent years. Developing an access control model and related mechanisms to support a selective access data become important. The extensible markup language (XML) is rapidly emerging as the new standard language for semi-structured data representation and exchange on the Internet. And now more and more information is distributed in XML format. In this article, we present a comprehensive approach for privacy preserving access control based on the notion of purpose. In our model, purpose information associated with a given data elements in an XML document specifies the intended use of the data elements. An important issue addressed in this article is the granularity of data labeling for data elements in XML documents and tree databases with which purposes can be associated. We address this issue in XML databases and propose different labeling schemes for XML documents. We also propose an approach to represent purpose information to support access control based on purpose information. Our proposed solution relies on usage access control (UAC) models as well as the components which based on the notions of the purpose information used in subjects and objects. Finally, comparisons with related works are analysed.

This paper proposes a trust involved management framework for supporting privacy preserving access control policies and mechanisms. The mechanism enforces access policy to data containing personally identifiable information. The key component of the framework is an access control model that provides full support for expressing highly complex privacy related policies, taking into account features like purposes and obligations. A policy refers to an access right that a subject can have on an object, based on relationship, trust, purpose and obligations. The structure of purpose involved access control policy is studied. Finally a discussion of our work in comparison with other access control and frameworks such as EPAL is discussed.

This paper focuses on the challenges on the privacy of Radio Frequency Identification (RFID) systems. RFID systems have already widely applied in industry and have been bringing lots of benefits to our daily life, it also creates new security and privacy problems to individuals and organizations. The security and privacy challenges are analysed after a brief introduction of various RFID systems and their associated operations. A proposal to protecting security and privacy of customers, as a solution of the challenge for low-cost RFID systems is designed. Finally, comparisons to related works of the proposal are described.

P-sensitive k-anonymity model has been recently defined as a sophistication of k-anonymity. This new property requires that there be at least p distinct values for each sensitive attribute within the records sharing a combination of key attributes. However, as shown in this paper, it may not protect sensitive information in some way. In this paper, we empirically investigate two enhanced k-anonymity models. Instead of publishing original specific sensitive attributes, the new models publish the categories that the sensitive values belong to. We propose a top-down approach to implement two enhanced models and show in the comprehensive experimental evaluations that the two new introduced models are practical in terms of effectiveness and efficiency.

With an increasing amount of semi-structured data XML has become important. XML documents may contain private information that cannot be shared by all user communities. Therefore, securing XML data is becoming important. Several specifications progressed toward providing a comprehensive standards framework for securing XML-based application have been presented. These applications can be effective to protect information in a website. In this paper, we present XML and Web service security main standards and most specifications for these standards. Each standard which connects with protecting XML based documents is discussed, especially we present XML undeniable signature as an application with XML digital signature. We also briefly describe the relations with these standards based on existing technologies. Finally, comparisons with related works are analyzed.

Coverage control is a basic research problem in wireless sensor networks (WSNs). This paper proposes a distance optimization based coverage control algorithm, which utilizes ant colony algorithm to place sensor nodes to an appropriate position and optimize the moving path of mobile sensor nodes. The proposed algorithm not only guarantees the biggest coverage of wireless sensor networks, but also reduces node moving distance, which saves sensor node energy and prolongs the entire network lifetime. Simulation results prove its validity.

In this paper, a high order open and closed ILC (iterative learning control) scheme with initial state learning is presented. The convergent bounds are only dependent on the system uncertainties and disturbances but independent of the initialization errors. The scheme performs better than common ILC scheme with initial state learning both in convergence rate and transient performance. By adding closed loop, the whole algorithm has better performance in both stability and convergence than the open loop one alone. Furthermore, the effectiveness of the proposed method is illustrated by simulation experiments.