Update Operations Research Articles

Integrity Assurance Method of Multi-Keyword Query for Encrypted Outsourced Data

As the data scale exceeds the petabyte level, more and more users outsource their local databases to third-party service providers to save local storage costs and avoid cumbersome local data management. However, because third-party service providers are not fully trusted, they may leak outsourced data and bring security risks to the personal privacy of data users. The service provider may also return incorrect or incomplete query results to the data user. Therefore, in this paper, we propose a Lightweight and Verifiable Multi-keyword Query Scheme for the integrity verification of multi-keyword query of outsourced data. This scheme supports multi-keyword query integrity verification and does not require a third-party auditor, combining a hash-based accumulator that provides correctness verification and a cuckoo filter that provides integrity verification to form a provably secure composite verification structure, and at the same time, it uses controllable Paillier encryption to support data update operations. This scheme strikes a balance between the cost of verification structure generation, verification object generation, and proof verification cost to enable data users to perform secure multi-keyword query while efficiently verifying the integrity of query results. The security proof and the performance analysis show that the proposed scheme is secure and efficient for practical deployment.

Database Padding for Dynamic Symmetric Searchable Encryption

Dynamic symmetric searchable encryption (DSSE) that enables the search and update of encrypted databases outsourced to cloud servers has recently received widespread attention for leakage-abuse attacks against DSSE. In this paper, we propose a dynamic database padding method to mitigate the threat of data leakage during the update operation of outsourcing data. First, we introduce an outlier detection technology where bogus files are generated for padding according to the outlier factors, hiding the document information currently matching search keywords. Furthermore, we design a new index structure suitable for the padded database using the bitmap index to simplify the update operation of the encrypted index. Finally, we present an application scenario of the padding method and realize a forward and backward privacy DSSE scheme (named PDB-DSSE). The security analysis and simulation results show that our dynamic padding algorithm is suitable for DSSE scheme and PDB-DSSE scheme maintains the security and efficiency of the retrieval and update of the DSSE scheme.

Identity-Based Dynamic Data Auditing for Big Data Storage

Identity-based remote data auditing schemes can verify data integrity and provide a simple identity authentication and management for multiple users. However, prior works on identity-based remote data auditing lack the support of dynamic operations. In these schemes, tag generation is linked to the index of data block, which is related to update operations such as modification, insertion and deletion. If users perform dynamic operations on a data block, the tags of all subsequent blocks need to be modified. It means that if users want to update data on a big data platform, they have to download the whole file, update the file and send the updated file to the big data platform. Such pattern will bring huge communication overhead. In this paper, we propose an identity-based dynamic data auditing scheme which supports dynamic data operations, including modification, insertion and deletion. As far as we know, there is still no other identity-based data auditing scheme that supports dynamic operations. In particular, to achieve efficient dynamic operations, we use the data structure of Merkle hash tree for block tag authentication, which helps update data with integrity assurance. Analyses of security and performance show that the proposed scheme is efficient and secure.

A Secure Searchable Encryption Framework for Privacy-Critical Cloud Storage Services

Searchable encryption has received a significant attention from the research community with various constructions being proposed, each achieving asymptotically optimal complexity for specific metrics (e.g., search, update). Despite their elegance, the recent attacks and deployment efforts have shown that the optimal asymptotic complexity might not always imply practical performance, especially if the application demands a high privacy. In this article, we introduce a novel Dynamic Searchable Symmetric Encryption (DSSE) framework called Incidence Matrix (IM)-DSSE, which achieves a high level of privacy, efficient search/update, and low client storage with actual deployments on real cloud settings. We harness an incidence matrix along with two hash tables to create an encrypted index, on which both search and update operations can be performed effectively with minimal information leakage. This simple set of data structures surprisingly offers a high level of DSSE security while achieving practical performance. Specifically, IM-DSSE achieves forward-privacy, backward-privacy and size-obliviousness simultaneously. We also create several DSSE variants, each offering different trade-offs that are suitable for different cloud applications and infrastructures. We fully implemented our framework and evaluated its performance on a real cloud system (Amazon EC2). We have released IM-DSSE as an open-source library for wide development and adaptation.

Time and Energy Minimization Communications Based on Collaborative Beamforming for UAV Networks: A Multi-Objective Optimization Method

Unmanned aerial vehicle (UAV) communications and networks are of utmost concern. However, they have challenges such as the limited on-board energy and restricted transmit power. In this paper, we study a UAV-enabled communication scenario that a set of UAVs perform a virtual antenna array (VAA) to communicate with different remote base stations (BSs) by using collaborative beamforming (CB). To achieve a better transmission performance, the UAV elements can fly to optimal positions by using optimal speeds and adjust to optimal excitation current weights for performing CB transmissions. However, there are some trade-offs between energy consumption and transmission performance. Thus, we formulate a time and energy minimization communication multi-objective optimization problem (TEMCMOP) of CB in UAV networks to simultaneously minimize the total transmission time, total performing time of VAAs and total motion and hovering energy consumptions of UAVs by jointly optimizing the positions, flight speeds and excitation current weights of UAVs, as well as the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated TEMCMOP, we propose an improved multi-objective ant lion optimization (IMOALO) algorithm with chaos-opposition based learning solution initialization and hybrid solution update operators to solve the problem. Simulation results verify that the proposed IMOALO can effectively solve the formulated TEMCMOP and it has better performance than some other benchmark approaches.

Enabling Decentralized and Dynamic Data Integrity Verification for Secure Cloud Storage via T-Merkle Hash Tree Based Blockchain

Cloud storage provides elastic storage services for enterprises and individuals remotely. However, security problems such as data integrity are becoming a major obstacle. Recently, blockchain-based verification approaches have been extensively studied to get rid of a centralized third-party auditor. Most of these schemes suffer from poor scalability and low search efficiency and even fail to support data dynamic update operations on blockchain, which limits their large-scale and practical applications. In this work, we propose a blockchain-based dynamic data integrity verification scheme for cloud storage with T-Merkle hash tree. A decentralized scheme is proposed to eliminate the restrictions of previous centralized schemes. The data tags are generated by the technique of ZSS short signature and stored on blockchain. An improved verification method is designed to check the integrity of cloud data by transferring computation from a verifier to cloud server and blockchain. Furthermore, a storage structure called T-Merkle hash tree which is built based on T-tree and Merkle hash tree is designed to improve storage utilization of blockchain and support binary search on chain. Moreover, we achieve efficient and secure dynamic update operations on blockchain by an append-only manner. Besides, we extend our scheme to support batch verification to handle massive tasks simultaneously; thus, the efficiency is improved and communication cost is reduced. Finally, we implemented a prototype system based on Hyperledger Fabric to validate our scheme. Security analysis and performance studies show that the proposed scheme is secure and efficient.

Relevance in Belief Update

It has been pointed out by Katsuno and Mendelzon that the so-called AGM revision operators, defined by Alchourrón, Gärdenfors and Makinson, do not behave well in dynamically-changing applications. On that premise, Katsuno and Mendelzon formally characterized a different type of belief-change operators, typically referred to as KM update operators, which, to this date, constitute a benchmark in belief update. In this article, we show that there exist KM update operators that yield the same counter-intuitive results as any AGM revision operator. Against this non-satisfactory background, we prove that a translation of Parikh’s relevance-sensitive axiom (P), in the realm of belief update, suffices to block this liberal behaviour of KM update operators. It is shown, both axiomatically and semantically, that axiom (P) for belief update, essentially, encodes a type of relevance that acts at the possible-worlds level, in the context of which each possible world is locally modified, in the light of new information. Interestingly, relevance at the possible-worlds level is shown to be equivalent to a form of relevance that acts at the sentential level, by considering the building blocks of relevance to be the sentences of the language. Furthermore, we concretely demonstrate that Parikh’s notion of relevance in belief update can be regarded as (at least a partial) solution to the frame, ramification and qualification problems, encountered in dynamically-changing worlds. Last but not least, a whole new class of well-behaved, relevance-sensitive KM update operators is introduced, which generalize Forbus’ update operator and are perfectly-suited for real-world implementations.

Buffer-Assisted Network Updates in Timed SDN

Although the logically-centralized perspective is offered in Software-Defined Networking (SDN), the data plane is still distributed. Update commands sent by the centralized controller are executed asynchronously and independently in each switch. The timed SDN enables synchronous and coordinated update operations as each update command can be triggered by a pre-defined timestamp. Prior work on timed update mainly focuses on producing a congestion-free update sequence, whereas finding a congestion-free timed update sequence may prolong the update time. Even worse, such an update order may not exist. In this paper, we propose Chronus <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , a novel timed update system that utilizes the switch buffer to shorten the update time while minimizing the switch buffer during updates. First, we formulate the Minimum Switch Buffer Problem (MSBP) as an integer linear programming and show its hardness. Then, we propose a set of efficient algorithms to solve the problem in polynomial time. Extensive evaluations in Mininet and large-scale simulations show that Chronus <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> can reduce the update time by at least 17% and the switch buffer size by at least 27% compared with state-of-the-art approaches.

Improving the support for XML dynamic updates using a hybridization labeling scheme (ORD-GAP)

Background : As the standard for the exchange of data over the World Wide Web, it is important to ensure that the eXtensible Markup Language (XML) database is capable of supporting not only efficient query processing but also capable of enduring frequent data update operations over the dynamic changes of Web content. Most of the existing XML annotation is based on a labeling scheme to identify each hierarchical position of the XML nodes. This computation is costly as any updates will cause the whole XML tree to be re-labelled. This impact can be observed on large datasets. Therefore, a robust labeling scheme that avoids re-labeling is crucial. Method: Here, we present ORD-GAP (named after Order Gap), a robust and persistent XML labeling scheme that supports dynamic updates. ORD-GAP assigns unique identifiers with gaps in-between XML nodes, which could easily identify the level, Parent-Child (P-C), Ancestor-Descendant (A-D) and sibling relationship. ORD-GAP adopts the OrdPath labeling scheme for any future insertion. Results: We demonstrate that ORD-GAP is robust enough for dynamic updates, and have implemented it in three use cases: (i) left-most, (ii) in-between and (iii) right-most insertion. Experimental evaluations on DBLP dataset demonstrated that ORD-GAP outperformed existing approaches such as ORDPath and ME Labeling concerning database storage size, data loading time and query retrieval. On average, ORD-GAP has the best storing and query retrieval time. Conclusion: The main contributions of this paper are: (i) A robust labeling scheme named ORD-GAP that assigns certain gap between each node to support future insertion, and (ii) An efficient mapping scheme, which built upon ORD-GAP labeling scheme to transform XML into RDB effectively.

Improving the support for XML dynamic updates using a hybridization labeling scheme (ORD-GAP).

Background: As the standard for the exchange of data over the World Wide Web, it is important to ensure that the eXtensible Markup Language (XML) database is capable of supporting not only efficient query processing but also capable of enduring frequent data update operations over the dynamic changes of Web content. Most of the existing XML annotation is based on a labeling scheme to identify each hierarchical position of the XML nodes. This computation is costly as any updates will cause the whole XML tree to be re-labelled. This impact can be observed on large datasets. Therefore, a robust labeling scheme that avoids re-labeling is crucial. Method: Here, we present ORD-GAP (named after Order Gap), a robust and persistent XML labeling scheme that supports dynamic updates. ORD-GAP assigns unique identifiers with gaps in-between XML nodes, which could easily identify the level, Parent-Child (P-C), Ancestor-Descendant (A-D) and sibling relationship. ORD-GAP adopts the OrdPath labeling scheme for any future insertion. Results: We demonstrate that ORD-GAP is robust enough for dynamic updates, and have implemented it in three use cases: (i) left-most, (ii) in-between and (iii) right-most insertion. Experimental evaluations on DBLP dataset demonstrated that ORD-GAP outperformed existing approaches such as ORDPath and ME Labeling concerning database storage size, data loading time and query retrieval. On average, ORD-GAP has the best storing and query retrieval time. Conclusion: The main contributions of this paper are: (i) A robust labeling scheme named ORD-GAP that assigns certain gap between each node to support future insertion, and (ii) An efficient mapping scheme, which built upon ORD-GAP labeling scheme to transform XML into RDB effectively.

Multidirection Update-Based Multiobjective Particle Swarm Optimization for Mixed No-Idle Flow-Shop Scheduling Problem

The Mixed No-Idle Flow-shop Scheduling Problem (MNIFSP) is an extension of flow-shop scheduling, which has practical significance and application prospects in production scheduling. To improve the efficacy of solving the complicated multiobjective MNIFSP, a MultiDirection Update (MDU) based Multiobjective Particle Swarm Optimization (MDU-MoPSO) is proposed in this study. For the biobjective optimization problem of the MNIFSP with minimization of makespan and total processing time, the MDU strategy divides particles into three subgroups according to a hybrid selection mechanism. Each subgroup prefers one convergence direction. Two subgroups are individually close to the two edge areas of the Pareto Front (PF) and serve two objectives, whereas the other one approaches the central area of the PF, preferring the two objectives at the same time. The MDU-MoPSO adopts a job sequence representation method and an exchange sequence-based particle update operation, which can better reflect the characteristics of sequence differences among particles. The MDU-MoPSO updates the particle in multiple directions and interacts in each direction, which speeds up the convergence while maintaining a good distribution performance. The experimental results and comparison of six classical evolutionary algorithms for various benchmark problems demonstrate the effectiveness of the proposed algorithm.

C-trie++: A dynamic trie tailored for fast prefix searches

Given a dynamic set K of k strings of total length n whose characters are drawn from an alphabet of size σ, a keyword dictionary is a data structure built on K that provides lookup, prefix search, and update operations on K. Under the assumption that α=w/lg⁡σ characters fit into a single machine word of w bits, we propose a keyword dictionary that represents K in either nlg⁡σ+Θ(klg⁡n) or |T|lg⁡σ+Θ(kw) bits of space, where |T| is the number of nodes of a trie representing K. It supports all operations in O(m/α+lg⁡α) expected time on an input string of length m in the word RAM model. An evaluation of our implementation highlights the practical usefulness of the proposed data structure, especially for prefix searches — one of the most essential keyword dictionary operations.

Multiple-level threshold load balancing in distributed SDN controllers

The distributed Software-Defined Networking (SDN) approach has been adopted to address the scalability issue linked to the use of a single SDN controller, particularly in large-scale networks. However, this method gives rise to a new challenge of uneven load balance on the distributed SDN controller. Several studies tried to solve this issue but they have focused only on balancing the load when an overload occurs and have not achieved the load balance continuously. In this paper, we propose the multiple threshold load balance (MTLB) switch migration scheme to achieve continuous load balancing among the controllers. This approach classifies the load into several gradual levels that represents the basis for the switch migration when the load of a controller differs from others, which lead the threshold value to be dynamically adjusted. Moreover, our scheme reduces the overhead of unwanted update operations among the controllers by adopting the controller load status as an indicator for disseminating the load information. We implement our scheme using the Floodlight controller and Mininet emulator. The experimental results show that our scheme has better comprehensive performance than the other schemes in terms of response time, control overhead, and throughput rate.

Achieving Fast Loop-Free Updates With Ingress Port in Software-Defined Networks

Due to the distributed and asynchronous nature in data plane, the packets can be forwarded into a loop during routing updates. Software-Defined Networks (SDNs) enable a controller to schedule the update operations of routing rules in a loop-free manner. However, the existing rule replacement mechanism cannot give an efficient solution for large-scale update scenarios. In this paper, we make use of the ingress port field in forwarding rules to design a loop-free update scheme, Inport-Matching Update (IMU), for per-flow and destination-based unicast, which significantly improves efficiency of solving update scenarios and reduces update rounds. In both our proposed and previous mechanisms, a subproblem called Rule Replacement Scheduling (RRS) problem needs to be solved and we prove that the scale of RRS problem can be reduced at least by half. Moreover, we use the inclusion-exclusion principle to rigorously prove that the probability that an RRS problem under IMU has a trivial solution, i.e., one round, is always higher than 83.7%, given a random per-flow unicast update scenario with ingress port rules. Experimental results show that our proposed Inport-Matching Update mechanism remarkably reduces the average number of rounds and runtime for solving the RRS problem compared to the existing rule replacement mechanism.

A general multi-agent epistemic planner based on higher-order belief change

In recent years, multi-agent epistemic planning has received attention from both dynamic logic and planning communities. Existing implementations of multi-agent epistemic planning are based on compilation into classical planning and suffer from various limitations, such as generating only linear plans, restriction to public actions, and incapability to handle disjunctive beliefs. In this paper, we consider centralized multi-agent epistemic planning from the viewpoint of a third person who coordinates all the agents to achieve the goal. We treat contingent planning, resulting in nonlinear plans. We model private actions and hence handle beliefs, formalized with the multi-agent KD45 logic. We handle static propositional common knowledge, which we call constraints. For such planning settings, we propose a general representation framework where the initial knowledge base (KB) and the goal, the preconditions and effects of actions can be arbitrary KD45n formulas, and the solution is an action tree branching on sensing results. In this framework, the progression of KBs w.r.t. actions is achieved through the operation of belief revision or update on KD45n formulas, that is, higher-order belief revision or update. To support efficient reasoning and progression, we make use of a normal form for KD45n called alternating cover disjunctive formulas (ACDFs). We propose reasoning, revision and update algorithms for ACDFs. Based on these algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MEPK. Our experimental results show the viability of our approach.

Guiding the evolution of product-line configurations

A product line is an approach for systematically managing configuration options of customizable systems, usually by means of features. Products are generated for configurations consisting of selected features. Product-line evolution can lead to unintended changes to product behavior. We illustrate that updating configurations after product-line evolution requires decisions of both, domain engineers responsible for product-line evolution as well as application engineers responsible for configurations. The challenge is that domain and application engineers might not be able to interact with each other. We propose a formal foundation and a methodology that enables domain engineers to guide application engineers through configuration evolution by sharing knowledge on product-line evolution and by defining automatic update operations for configurations. As an effect, we enable knowledge transfer between those engineers without the need for interactions. We evaluate our methodology on four large-scale industrial product lines. The results of the qualitative evaluation indicate that our method is flexible enough for real-world product-line evolution. The quantitative evaluation indicates that we detect product behavior changes for up to 55.3% of the configurations which would not have been detected using existing methods.

Communication Pattern Models: An Extension of Action Models for Dynamic-Network Distributed Systems

Halpern and Moses were the first to recognize, in 1984, the importance of a formal treatment of knowledge in distributed computing. Many works in distributed computing, however, still employ informal notions of knowledge. Hence, it is critical to further study such formalizations. Action models, a significant approach to modeling dynamic epistemic logic, have only recently been applied to distributed computing, for instance, by Goubault, Ledent, and Rajsbaum. Using action models for analyzing distributed-computing environments, as proposed by these authors, has drawbacks, however. In particular, a direct use of action models may cause such models to grow exponentially as the computation of the distributed system evolves. Hence, our motivation is finding compact action models for distributed systems. We introduce communication pattern models as an extension of both ordinary action models and their update operator. We give a systematic construction of communication pattern models for a large variety of distributed-computing models called dynamic-network models. For a proper subclass of dynamic-network models called oblivious, the communication pattern model remains the same throughout the computation.

An improved artificial tree algorithm with two populations (IATTP)

Many new bio-inspired algorithms are recently being proposed, artificial tree (AT) algorithm, inspired by the growth of trees and the update behavior of branches, is one of them. There are also some improved AT algorithms being proposed to improve their calculation accuracy. However, the main challenges of AT algorithms lie in the insufficiencies in the design of update operators as well as the position interaction between branches and the capture of key information and the performance of AT algorithms needs to be enhanced. This work proposes an improved AT algorithm with two populations (IATTP). In IATTP, the update strategies of branches are redesigned, and a variety of efficient update operators are designed and applied. The branch population is changed from one to two, and the competition mechanism between populations is proposed. Through the migration of branches between populations, the scale of population with better efficiency is expanded and the size of population with lower efficiency is reduced, thus a reasonable interaction between populations and branches is realized. With above strategies, the efficiency and accuracy of IATTP are significantly improved. The results of IATTP are proved to be advantageous when the performance of IATTP is compared with AT algorithm, improved artificial tree (IAT) algorithm and feedback artificial tree (FAT) algorithm through typical test problems. Meanwhile, the results of IATTP in current state are also preferable when IATTP is compared with other improved algorithms in high dimensional problems. The experimental results prove that IATTP is competitive in solving optimization problems.

Failure to support general practice

Practice NursingVol. 32, No. 6 EditorialFailure to support general practiceKelly NickallsKelly NickallsEditorSearch for more papers by this authorKelly NickallsPublished Online:2 Jun 2021https://doi.org/10.12968/pnur.2021.32.6.215AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Kanani N. Supporting people to access the care they need. 2021. https://www.england.nhs.uk/blog/supporting-people-to-access-the-care-they-need/ (accessed 25 May 2021) Google ScholarNHS Digital. Appointments in General Practice March 2021. 2021. https://digital.nhs.uk/data-and-information/publications/statistical/appointments-in-general-practice/march-2021 (accessed 25 May 2021) Google ScholarNHS England. Updated standard operating procedure (SOP) to support restoration of general practice services. 2021. https://www.england.nhs.uk/wp-content/uploads/2021/05/B0497-GP-access-letter-May-2021-FINAL.pdf (accessed 25 May 2021) Google Scholar FiguresReferencesRelatedDetails 2 June 2021Volume 32Issue 6ISSN (print): 0964-9271ISSN (online): 2052-2940 Metrics History Published online 2 June 2021 Published in print 2 June 2021 Information© MA Healthcare LimitedPDF download

A Performance Comparison of RESTful Applications Implemented in Spring Boot Java and MS.NET Core

RESTful is an emergent architectural style to develop web services/applications due to its lightweight nature. Though more and more organizations are adopting RESTful services, when it comes to choosing the right platform to implement one, teams usually gravitate towards reusing the existing skillset and prefer the platform that they currently support instead of looking for a better performing alternative. Considering that internal architecture and implementation of each platform can drastically affect its efficiency and performance, the objective of this research paper is to assess and compare the performance of RESTful web applications implemented in Spring Boot Java and MS.NET Core. An identical business use case to provide the basic four Create, Read, Update, and Delete (CRUD) operations was developed in both implementations. Apache JMeter 5.2.1, an automated performance testing tool is used in this experiment to create a swamp of concurrent virtual users to load both the applications at regular intervals. The comprehensive experimental results are presented at the end of the research paper.