Nonparametric regression imputation is commonly used in missing data analysis. However, it suffers from the curse of dimension. The problem can be alleviated by the explosive sample size in the era of big data, while the large-scale data size presents some challenges in the storage of data and the calculation of estimators. These challenges make the classical nonparametric regression imputation methods no longer applicable. This motivates us to develop two distributed nonparametric regression imputation methods. One is based on kernel smoothing and the other on the sieve method. The kernel-based distributed imputation method has extremely low communication cost, and the sieve-based distributed imputation method can accommodate more local machines. The response mean estimation is considered to illustrate the proposed imputation methods. Two distributed nonparametric regression imputation estimators are proposed for the response mean, which are proved to be asymptotically normal with asymptotic variances achieving the semiparametric efficiency bound. The proposed methods are evaluated through simulation studies and illustrated in real data analysis.

Abstract The use of computer vision for fish monitoring in aquaculture fisheries has gained importance. It is crucial to obtain the object box, instance mask and landmarks of the fish to determine their status. There are well‐established methods to achieve these tasks, but running them in a serial sequence is inefficient and complex. A multi‐tasking framework is proposed that can implement the above three tasks in parallel, FishNet. Unlike other multi‐tasking frameworks that use one encoder with multiple decoders, the authors use only one encoder and one decoder to achieve multi‐tasking fusion and can be trained end‐to‐end. A multi‐task dataset for fish is produced to validate the framework. It achieved the best speed‐accuracy balance on object detection (a 95.3% box AP), instance segmentation (a 53.9% mask AP) and pose recognition (95.1% OKS AP), and reached real‐time inference speed (66.3 FPS) on the NVIDIA Tesla V100.

Remote data secure storage is of crucial importance in cloud computing. In order to check remote data integrity, an important paradigm PDP (i.e., provable data possession) is proposed. All the existing PDP schemes make use of RSA or bilinear pairings. One large file has to be divided into a great many of blocks. For example, 1T (Terabit) file has to be divided into$1.0737 \times 10^9$blocks (RSA where the length of the index is 1024 bits) or$6.8719 \times 10^9$blocks (bilinear pairings where the order of the elliptic curve is 160 bits). The huge computation cost and communication cost incurs the inefficient PDP implementation. In other words, they are not practical. In order to solve the problem, we propose a new PDP model: blockchain-based private PDP. The new concept makes use of blockchain which is the core of cryptocurrency. For the new concept, the paper formalizes its system model and security model. Then, a concrete blockchain-based private PDP scheme is designed by making use of blockchain and RSA. The proposed blockchain-based private PDP scheme is provably secure. At the same time, we also analyze its performance from two parts: theory analysis and implementation prototype. Our analysis shows that the proposed PDP scheme is secure, efficient and practical.

With the rapid development of cloud computing, more and more enterprises would like to upload and store their data in the public cloud. When the parts of the business of an enterprise are purchased by another enterprise, the corresponding data will be transferred to the acquiring enterprise. For the usual case, how to outsource the computation cost of data transfer to the cloud? How to ensure the remote purchased data integrity? Thus, it is important to study provable data possession with outsourced data transfer (DT-PDP). In this paper, for the first time, we propose the novel concept: DT-PDP. By taking use of DT-PDP, the following three security requirements can be satisfied: (1) the other un-purchased data security of acquired enterprise can be ensured; (2) the purchased data integrity and privacy can be ensured; (3) the data transferability’s computation can be outsourced to the public cloud servers. For the security concept of DT-PDP, we give its motivation, system model and security model. Then, we design a concrete DT-PDP scheme based on the bilinear pairings. At last, we analyze the security, efficiency and flexibility of the concrete DT-PDP scheme. It shows that our scheme is provably secure and efficient.

In vehicle-to-grid (V2G) networks, battery-powered vehicle (BV) provides service to the power grid. In order to encourage more BVs to provide the service for power grid, it is necessary to reward the BVs from the power grid. To extensively deploy V2G networks, some security and privacy problems must be solved. In this paper, for the first time, we propose the novel concept of blockchain-based anonymous rewarding scheme (BBARS) for V2G networks. The novel concept comes from the application requirement which has not been solved by now. We give the formal system model and security model of BBARS. Then, we design the concrete BBARS scheme by making use of two different public key cryptosystem. Through security analysis and performance analysis, the designed scheme is provably secure and efficient. The analysis results also show the designed BBARS scheme is practical for secure V2G networks in smart grid.

With the rapid development of information technology and the Internet of Things Technology (IOT), data security and healthy privacy are getting a lot of attention. In order to store, access, and share electronic health records, storage of this data is transferred to a third-party-cloud server. The security and privacy of electronic health records stored at date center or cloud server are not guaranteed. Before being sent to date center or cloud server, this data should be encrypted. Designing an efficient and secure fine-grained access control strategy for personal health records is facing enormous challenges. Security and privacy for electronic health records are very important because the electronic health data which plays an important role in medical server and treatment is directly associated with a particular patient. Attribute-based encryption (ABE) can effectively achieve fine-grained access control. However, the computation of bilinear pairings requires a large amount of computation overhead in ABE scheme. In order to decrease the computational overhead and ensure the confidentiality of electronic health records, a distributed fine-grained access control scheme with outsourced computation for IOT is proposed in this paper. Little calculation is executed by the receiver and sender in our proposed scheme. Outsourcing computing reduces the computing burden. The analyses of safety and performance show that our proposed scheme is safe and effective compared with previous schemes.

Anonymous multireceiver encryption scheme can not only protect the privacy of the receiver but also ensure the security of message. However, the computational cost of this scheme is very large. It is not suitable for the sender which has limited resources, such as mobile devices and sensor nodes. In this work, an anonymous multireceiver online/offline identity‐based encryption is proposed based on offline/online and identity‐based encryption (IBE). In identity‐based encryption scheme, the sender can encrypt the message using the unique information of the user (such as identity number or e‐mail address) as its public key. The receiver obtains the private key from a central authority. For mobile device with limited resource, the online/offline encryption scheme can reduce the computational cost. Compared to the previous anonymous multireceiver schemes, the proposed scheme can efficiently encrypt message with offline/online method and ensure the anonymity of receivers. The analysis results also show that our scheme is efficient in terms of computational cost by comparing to the previous works.

Abstract Outsourcing decryption that enables the authorized users to obtain the original data without decryption computation is crucially important for wireless sensor networks in public data center. The existing outsourcing decryption schemes have been designed based on key‐policy attribute‐based encryption. The security of outsourcing decryption cannot be guaranteed, because the data center is not loyal, and existing schemes have high computational complexity and energy consumption. In this work, a novel partially outsourcing decryption scheme is proposed to guarantee data security and computational efficiency for resource‐constrained sensor nodes and terminal equipments. According to the attributes of cluster nodes in the proposed scheme, the encryption secret key is encrypted based on group key‐policy attribute‐based encryption and sent to data center, and authorized users who satisfy the attributes of the ciphertext can obtain the secret key to decrypt the ciphertext. Furthermore, in order to reduce the decryption overhead for users, the authorized users can simply decrypt the transformation ciphertext that is partially decrypted by the data center using token key. Compared with the previous decryption schemes, the proposed scheme efficiently decrypts ciphertext and enhances security of the data. The simulation results also indicate that the proposed scheme is efficient in terms of energy consumption and computation by comparing to previous work. Copyright © 2016 John Wiley & Sons, Ltd.

In traditional access control systems, security administrators determine whether an information consumer can access a certain resource. However, in reality, it is very difficult for policy makers to foresee what information a user may need in various situations. In hospitals, failing to authorize a doctor for the medical information she needs about a patient could lead to severe or fatal consequences. In this paper, we propose a practical access control approach to protect patient privacy in health information systems by taking the realities in healthcare into consideration. First, unlike traditional access control systems, our proposed access control model allows information consumers (i.e. doctors) to make access decisions, while still being able to detect and control the over-accessing of patients' medical data by quantifying the risk associated with doctors' data-accessing activities. Second, we do not require doctors to do anything special in order to use our system. We learn about common practices among doctors and apply statistical methods and information theory techniques to quantify the risk of privacy violation. Third, occasional exceptions on information needs, which is common in healthcare, is taken into account in our model. We have implemented a prototype of our solution and performed simulations on real-world medical history records.

With the growing popularity of cloud computing, more and more enterprises are migrating their collaboration platforms from in-enterprise systems to Software as a Service (SaaS) applications. While SaaS collaboration has numerous advantages, it also raises new security challenges. In particular, since SaaS collaboration is increasingly used across enterprise boundaries, organizations are concerned that sensitive information may be leaked to outsiders due to their employees' inadvertent mistakes on information sharing. In this article, we propose to mitigate the data leakage problem in SaaS collaboration systems by reducing human errors. Built on top of the discretionary access control model in existing collaboration systems, we have designed a series of mechanisms to provide defense in depth against information leakage. First, we allow enterprises to encode their organizational security rules as mandatory access control policies, so as to impose coarse-grained restrictions on their employees' discretionary sharing decisions. Second, we design an attribute-based recommender that suggests and prioritizes potential recipients for users' files, reducing errors in the choices of recipients. Third, our system actively examines abnormal recipients entered by a file owner, providing the last line of defense before a file is shared. We have implemented a prototype of our solution and performed experiments on data collected from real-world collaboration systems.

In practice, assigning access permissions to users must satisfy a variety of constraints motivated by business and security requirements. Here, we focus on Role-Based Access Control (RBAC) systems, in which access permissions are assigned to roles and roles are then assigned to users. User-role assignment is subject to role-based constraints, such as mutual exclusion constraints, prerequisite constraints, and role-cardinality constraints. Also, whether a user is qualified for a role depends on whether his/her qualification satisfies the role's requirements. In other words, a role can only be assigned to a certain set of qualified users. In this paper, we study fundamental problems related to access control constraints and user-role assignment, such as determining whether there are conflicts in a set of constraints, verifying whether a user-role assignment satisfies all constraints, and how to generate a valid user-role assignment for a system configuration. Computational complexity results and/or algorithms are given for the problems we consider.

The web has largely become a very social environment and will continue to become even more so. People are not only enjoying their social visibility on the Web but also increasingly participating in various social activities delivered through the Web. In this paper, we propose to explore a user's public social activities, such as blogging and social bookmarking, to personalize Internet services. We believe that public social data provides a more acceptable way to derive user interests than more private data such as search histories and desktop data. We propose a framework that learns about users' preferences from their activities on a variety of online social systems. As an example, we illustrate how to apply the user interests derived by our system to personalize search results. Furthermore, our system is adaptive; it observes users' choices on search results and automatically adjusts the weights of different social systems during the information integration process, so as to refine its interest profile for each user. We have implemented our approach and performed experiments on real-world data collected from three large-scale online social systems. Over two hundred users from worldwide who are active on the three social systems have been tested. Our experimental results demonstrate the effectiveness of our personalized search approach. Our results also show that integrating information from multiple social systems usually leads to better personalized results than relying on the information from a single social system, and our adaptive approach further improves the performance of the personalization solution.

People are increasingly using more and more social softwares, generating flooding communications. User analytics may be performed to mine a person's activities on different social systems and extract patterns, be it interest patterns, social patterns, or work patterns. Such patterns may benefit both the individuals and the organizations the users associated with, as the information is valuable in numerous tasks, including recommendation, evaluation, management, and so on. In this article, we present an actionable solution of user analytics, namely collaboration analytics, by focusing on mining a person's work patterns from her collaboration activities. Our solution effectively makes use of a user's heterogeneous data collected from various collaboration tools to derive an integrated description of the user's collaborative work. A number of ``work areas'', each of which contains its work topics and people involved, are generated for every user. The challenges we face include the clustering of items with short texts and prioritizing/weighting data items based on importance/relevance. Our solutions to those issues will be described in this article. In particular, we mine users' background information from various types of data and use such information to enrich the semantics of the short texts contained in the activity instances on collaboration tools before clustering those instances into work areas. Finally, we have developed a prototype of our collaboration analytics solution and evaluated it with real-world data and people.

With the growing adoption of Role-Based Access Control (RBAC) in commercial security and identity management products, how to facilitate the process of migrating a non-RBAC system to an RBAC system has become a problem with significant business impact. Researchers have proposed to use data mining techniques to discover roles to complement the costly top-down approaches for RBAC system construction. An important problem is how to construct RBAC systems with low complexity. In this article, we define the notion of weighted structural complexity measure and propose a role mining algorithm that mines RBAC systems with low structural complexity. Another key problem that has not been adequately addressed by existing role mining approaches is how to discover roles with semantic meanings. In this article, we study the problem in two primary settings with different information availability. When the only information is user-permission relation, we propose to discover roles whose semantic meaning is based on formal concept lattices. We argue that the theory of formal concept analysis provides a solid theoretical foundation for mining roles from a user-permission relation. When user-attribute information is also available, we propose to create roles that can be explained by expressions of user-attributes. Since an expression of attributes describes a real-world concept, the corresponding role represents a real-world concept as well. Furthermore, the algorithms we propose balance the semantic guarantee of roles with system complexity. Finally, we indicate how to create a hybrid approach combining top-down candidate roles. Our experimental results demonstrate the effectiveness of our approaches.

We propose the role-and-relation-based access control (R 2 BAC) model for workflow authorization systems. In R 2 BAC, in addition to a user’s role memberships, the user’s relationships with other users help determine whether the user is allowed to perform a certain step in a workflow. For example, a constraint may require that two steps must not be performed by users who have conflicts of interests. We study computational complexity of the workflow satisfiability problem, which asks whether a set of users can complete a workflow. In particular, we apply tools from parameterized complexity theory to better understand the complexities of this problem. Furthermore, we reduce the workflow satisfiability problem to SAT and apply SAT solvers to address the problem. Experiments show that our algorithm can solve instances of reasonable size efficiently. Finally, it is sometimes not enough to ensure that a workflow can be completed in normal situations. We study the resiliency problem in workflow authorization systems, which asks whether a workflow can be completed even if a number of users may be absent. We formally define three levels of resiliency in workflow systems and study computational problems related to these notions of resiliency.

Many access control policy languages, e.g., XACML, allow a policy to contain multiple sub-policies, and the result of the policy on a request is determined by combining the results of the sub-policies according to some policy combining algorithms (PCAs). Existing access control policy languages, however, do not provide a formal language for specifying PCAs. As a result, it is difficult to extend them with new PCAs. While several formal policy combining algebras have been proposed, they did not address important practical issues such as policy evaluation errors and obligations; furthermore, they cannot express PCAs that consider all sub-policies as a whole (e.g., weak majority or strong majority). We propose a policy combining language PCL, which can succinctly and precisely express a variety of PCAs. PCL represents an advancement both in terms of theory and practice. It is based on automata theory and linear constraints, and is more expressive than existing approaches. We have implemented PCL and integrated it with SUN's XACML implementation. With PCL, a policy evaluation engine only needs to understand PCL to evaluate any PCA specified in it.

While many role mining algorithms have been proposed in recent years, there lacks a comprehensive study to compare these algorithms. These role mining algorithms have been evaluated when they were proposed, but the evaluations were using different datasets and evaluation criteria. In this paper, we introduce a comprehensive framework for evaluating role mining algorithms. We categorize role mining algorithms into two classes based on their outputs; Class 1 algorithms output a sequence of prioritized roles while Class 2 algorithms output complete RBAC states. We then develop techniques that enable us to compare these algorithms directly. We also introduce a new role mining algorithm and two new ways for algorithmically generating datasets for evaluation. Using synthetic as well as real datasets, we compared nine role mining algorithms. Our results illustrate the strengths and weaknesses of these algorithms.

We introduce the notion of resiliency policies in the context of access control systems. Such policies require an access control system to be resilient to the absence of users. An example resiliency policy requires that upon removal of any s users, there should still exist d disjoint sets of users such that the users in each set together possess certain permissions of interest. Such a policy ensures that even when emergency situations cause some users to be absent, there still exist independent teams of users that have the permissions necessary for carrying out critical tasks. The Resiliency Checking Problem determines whether an access control state satisfies a given resiliency policy. We show that the general case of the problem and several subcases are intractable ( NP -hard), and identify two subcases that are solvable in linear time. For the intractable cases, we also identify the complexity class in the polynomial hierarchy to which these problems belong. We discuss the design and evaluation of an algorithm that can efficiently solve instances of nontrivial sizes that belong to the intractable cases of the problem. Furthermore, we study the consistency problem between resiliency policies and static separation of duty policies. Finally, we combine the notions of resiliency and separation of duty to introduce the resilient separation of duty policy, which is useful in situations where both fault-tolerance and fraud-prevention are desired.

With the growing adoption of role-based access control (RBAC) in commercial security and identity management products, how to facilitate the process of migrating a non-RBAC system to an RBAC system has become a problem with significant business impact. Researchers have proposed to use data mining techniques to discover roles to complement the costly top-down approaches for RBAC system construction. A key problem that has not been adequately addressed by existing role mining approaches is how to discover roles with semantic meanings. In this paper, we study the problem in two settings with different information availability. When the only information is user-permission relation, we propose to discover roles whose semantic meaning is based on formal concept lattices. We argue that the theory of formal concept analysis provides a solid theoretical foundation for mining roles from userpermission relation. When user-attribute information is also available, we propose to create roles that can be explained by expressions of user-attributes. Since an expression of attributes describes a real-world concept, the corresponding role represents a real-world concept as well. Furthermore, the algorithms we proposed balance the semantic guarantee of roles with system complexity. Our experimental results demonstrate the effectiveness of our approaches.

The process of introducing security controls into a sensitive task, which we call secure task design in this article, consists of two steps: high-level security policy design and low-level enforcement scheme design. A high-level security policy states an overall requirement for a sensitive task. One example of a high-level security policy is a separation of duty policy, which requires a task to be performed by a team of at least k users. Unlike low-level enforcement schemes such as security constraints in workflows, a separation of duty policy states a high-level requirement about the task without referring to individual steps in the task. While extremely important and widely used, separation of duty policies state only requirements on the number of users involved in the task and do not capture the requirements on these users' attributes. In this article, we introduce a novel algebra that enables the formal specification of high-level policies that combine requirements on users' attributes with requirements on the number of users motivated by separation of duty considerations. We give the syntax and semantics of the algebra and study algebraic properties of its operators. After that, we study potential mechanisms to enforce high-level policies specified in the algebra and a number of computational problems related to policy analysis and enforcement.

Specifying and managing access control policies is a challenging problem. We propose to develop formal verification techniques for access control policies to improve the current state of the art of policy specification and management. In this paper, we formalize classes of security analysis problems in the context of Role-Based Access Control. We show that in general these problems are PSPACE-complete. We also study the factors that contribute to the computational complexity by considering a lattice of various subcases of the problem with different restrictions. We show that several subcases remain PSPACE-complete, several further restricted subcases are NP-complete, and identify two subcases that are solvable in polynomial time. We also discuss our experiences and findings from experimentations that use existing formal method tools, such as model checking and logic programming, for addressing these problems.

A high-level security policy states an overall safety requirement for a sensitive task. One example of a high-level security policy is a separation of duty policy, which requires a sensitive task to be performed by a team of at least k users. Recently, Li and Wang [6] proposed an algebra for specifying a wide range of high-level security policies with both qualification and quantity requirements on users who perform a task. In this paper, we study the problem of direct static enforcement of high-level security policies expressed in this algebra. We formally define the notion of a static safety policy, which requires that every set of users together having all permissions needed to complete a sensitive task must contain a subset that satisfies the corresponding security requirement expressed as a term in the algebra. The static safety checking problem asks whether an access control state satisfies a given high-level policy. We study several computational problems related to the static safety checking problem, and design and evaluate an algorithm for solving the problem.

We introduce the notion of resiliency policies in the context of access control systems. Such policies require an access control system to be resilient to the absence of users. An example resiliency policy requires that, upon removal of any s users, there should still exist d disjoint sets of users such that the users in each set together possess certain permissions of interest. Such a policy ensures that even when emergency situations cause some users to be absent, there still exist independent teams of users that have the permissions necessary for carrying out critical tasks. The Resiliency Checking Problem determines whether an access control state satisfies a given resiliency policy. We show that the general case of the problem and several subcases are intractable (NP-hard), and identify two subcases that are solvable in linear time. For the intractable cases, we also identify the complexity class in the polynomial hierarchy to which these problems belong. We discuss the design and evaluation of an algorithm that can efficiently solve instances of nontrivial sizes that belong to the intractable cases of the problem. Finally, we study the consistency problem between resiliency policies and static separation of duty policies.

A high-level security policy states an overall requirement for a sensitive task. One example of a high-level security policy is a separation of duty policy, which requires a sensitive task to be performed by a team of at least k users. It states a high-level requirement about the task without the need to refer to individual steps in the task. While extremely important and widely used, separation of duty policies state only quantity requirements and do not capture qualification requirements on users involved in the task. In this paper, we introduce a novel algebra that enables the specification of high-level policies that combine qualification requirements with quantity requirements motivated by separation of duty considerations. A high-level policy associates a task with a term in the algebra and requires that all sets of users that perform the task satisfy the term. We give the syntax and semantics of the algebra and study algebraic properties of its operators. We also study several computational problems related to the algebra.