Perform Search Operations Research Articles

Implementation of A Blockchain-based Searchable Encryption for Securing Contact Tracing Data

<p>We developed a blockchain-based multi-keyword searchable encryption scheme for securing COVID-19 contact tracing data. In this scheme, we used AES-GCM to encrypt the contact tracing data, guaranteeing that only authorized users can perform decryption. Our scheme employed a Ciphertext-Policy Attribute-based searchable encryption to encrypt the search index, ensuring only users with appropriate attributes can perform search operations. The scheme supports dynamic updates of the search index. The blockchain-based storage with smart contract ensures immutability and non-repudiation of storage and retrieval. Overall, the evaluation of the scheme shows that it works efficiently without compromising the security goals. This is one of the first works to implement a solution for secure storage and search of contact tracing data with blockchain-based searchable encryption. Compared to the existing searchable contact tracing schemes, it provides more features and maintains efficiency even if a large search index is used.</p> <p> </p>

Efficient keyword search on encrypted dynamic cloud data

Sensitive information is increasingly being outsourced to the cloud. In order to protect the privacy of such sensitive data, cloud users (clients) encrypt their data before outsourcing. However, this poses a difficulty to later perform search operations on the encrypted data. Searchable encryption schemes enable a client to search and retrieve the cloud data (based on the keywords present in the data) when the data is encrypted. Dynamic searchable encryption schemes allow the client to search over the encrypted cloud data even when new documents are added to or deleted from the encrypted data. There is a trade-off between security (that is measured in terms of information leaked to the cloud) and the efficiency of dynamic searchable encryption schemes. Stronger security guarantees often come at a cost of less efficiency.In this work, we propose a new dynamic searchable encryption scheme for cloud data that achieves better security guarantees and improved efficiency compared to popular dynamic searchable encryption schemes. Our scheme uses an efficient data structure that reduces storage, lookup (search) time, and database modification time. We build a prototype of our scheme and experiment on large real-life datasets. We show our scheme performs better than the existing schemes, which provide similar (or weaker) security.

Verifiable and Searchable Symmetric Encryption Scheme Based on the Public Key Cryptosystem

With the rapid development of Internet of Things technology and cloud computing technology, all industries need to outsource massive data to third-party clouds for storage in order to reduce storage and computing costs. Verifiable and dynamic searchable symmetric encryption is a very important cloud security technology, which supports the dynamic update of private data and allows users to perform search operations on the cloud server and verify the legitimacy of the returned results. Therefore, how to realize the dynamic search of encrypted cloud data and the effective verification of the results returned by the cloud server is a key problem to be solved. To solve this problem, we propose a verifiable dynamic encryption scheme (v-PADSSE) based on the public key cryptosystem. In order to achieve efficient and correct data updating, the scheme designs verification information (VI) for each keyword and constructs a verification list (VL) to store it. When dynamic update operations are performed on the cloud data, it is easy to quickly update the security index through obtaining the latest verification information in the VL. The safety and performance evaluation of the v-PADSSE scheme proved that the scheme is safe and effective.

Researching basic searchable encryption schemes in databases that support SQL

Currently, many users prefer to outsource data to third-party cloud servers in order to mitigate the load of local storage. However, storing sensitive data on remote servers creates security challenges and is a source of concern for data owners. With ever-growing security and privacy concerns, it is becoming increasingly important to encrypt data stored remotely. However, the use of traditional encryption prevents the search operation in the encrypted data. One approach to solving this problem is searchable encryption. Solutions for search in secure databases cover a wide range of cryptographic techniques, although there is still no dominant solution. Designing secure search systems is a balance between security, functionality, performance, and usability. Therefore, this paper provides an overview of some of the important current secure search solutions. The main searchable encryption systems of databases that support SQL are considered. The strengths and weaknesses of the analyzed systems and the techniques implemented in them are highlighted. A comparative analysis of some characteristics of the compared systems is given. Attention is drawn to the fact that the ability to perform search operations in encrypted data leads to a complication of systems, an increase in the amount of required memory and query execution time. All this indicates the openness of the protected search problem and the need for further research in this direction to ensure secure work with remote databases and data warehouses.

Verifiable Keyword Search Supporting Sensitive Information Hiding for the Cloud-Based Healthcare Sharing System

With the integration of the healthcare system, Internet of Things, and cloud storage service, more and more medical institutions upload their electronic medical records (EMRs) to the cloud to reduce the local storage burden and realize data sharing among external researchers. To secure the sensitive information, EMRs usually should be encrypted before being stored on the cloud. However, the existing searchable encryption schemes that encrypt the entire EMRs can hide the sensitive information, but this results in the shared EMRs being unable to be used by researchers. In addition, if the queried and extracted EMRs are incorrect, it will lead to misdiagnosis and even endanger the patient’s life. In order to solve the aforementioned problems, in this article, we propose a verifiable keyword search scheme supporting sensitive information hiding for the cloud-based healthcare sharing system. The sensitive information is encrypted, while other contents in EMR can be shared among users in this scheme. Doctors and researchers can quickly perform search operations based on keywords to extract the EMRs they require. This time complexity is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n)$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of attribute values in the record. But the sensitive information is hidden for the researchers. Furthermore, the correctness of EMRs can be verified when they are extracted from the cloud. This time complexity is max <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lbrace O(n^{\prime }),O(N^{\prime })\rbrace$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n^{\prime }$</tex-math></inline-formula> is the number of query keywords and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N^{\prime }$</tex-math></inline-formula> is the number of the retrieved records. We expound the security and carry out experiments to estimate the efficiency of the proposed scheme.

A Blockchain-Enabled Privacy-Preserving Verifiable Query Framework for Securing Cloud-Assisted Industrial Internet of Things Systems

Advanced Industrial Internet-of-Things (IIoT), such as smart grids, 5G-enabled unmanned aerial vehicles (UAV), and supply chain 4.o, can be used to facilitate smart management. Nevertheless, IIoT systems generate huge amounts of data that need to be outsourced to the cloud for storing and providing real-time search facilities to end-users. Outsourcing IIoT data to a third-party cloud service provider (CSP) introduces several data privacy and integrity issues related to verifying the reliability of users’ queries and aggregated outcomes. In this article, we propose a blockchain-based framework for provisioning a privacy-preserving and verifiable query facility to end-users in IIoT systems. The framework uses blockchain to store IoT data as on-chain data and the cloud to store extensive data (e.g., image) as off-chain data and provisioning search services to users by executing a query in both on-chain and off-chain data and generating an aggregated result. Besides, it introduces a new privacy-preserving query mechanism for ensuring sensitive data privacy during query execution. A data owner encrypts both on-chain and off-chain data in the privacy-preserving query mechanism before sending it to the blockchain and cloud. A CSP can perform search operations on the encrypted on-chain and off-chain data to ensure sensitive data privacy. A multisignature-powered query verification model is also built for the blockchain. The query verification model allows each blockchain node to endorse the query result individually and a user to verify the endorsement of the query result before use. The experiments revealed the high efficiency and scalability of the proposed framework.

Research on the main methods and schemes of encryption with search capability

The growing popularity of data outsourcing to third-party cloud servers causes their owners to have serious concerns about their security due to possible data leakage. A well-known measure to solve this problem and ensure the confidentiality of data is to encrypt it. However, the use of traditional encryption techniques is faced with the problem of how to allow untrusted cloud servers to perform search operations, while the actual data transmitted must remain confidential. Searchable encryption is a powerful tool, a class of cryptographic techniques that attempts to solve this problem. Searchable encryption acts as a data management technique that allows data owners to store and manage their data on a third-party, untrusted cloud server, and allows the data user to delegate search functions to the cloud server to retrieve that data. Currently, there are a number of approaches to solving this problem, although there is still no dominant solution. Therefore, the paper presents an overview of current secure search solutions. The main searchable encryption techniques are considered, which allow you to perform search operations on encrypted data without disclosing any information about what is being searched. The strengths and weaknesses of the analyzed methods are highlighted. Models and architectures of existing secure search engines are analyzed, taking into account the peculiarities of their operation scenarios. The problem of confidentiality in searchable encryption schemes is discussed. A comparative analysis of the performance of several searchable symmetric encryption schemes is given. Various gaps in the area under consideration are identified, with indication of open research problems.

Efficient circuit design for content-addressable memory in quantum-dot cellular automata technology

Quantum-dot cellular automata (QCA) technology is a kind of nanotechnology utilized for building computational circuits. It can be a good technology for overcome CMOS drawbacks at nano-scale due to its low delay and area. The Content-Addressable Memory (CAM) is a very fast memory that can perform search operations in a very short time. This feature makes the relative popularity of these memories and many applications for them, especially in network routing and processors. In this study, a novel loop-based circuit is designed for the QCA memory unit, which reduces area, cell count, latency, and cost. The obtained results using QCADesigner tool version 2.0.3 demonstrate that the designed QCA memory unit utilizes 16 cells, 0.01 µm2 area, and 0.25 clock cycles and has a reduction of 33% in the number of cells, 50% in area, 50% in latency, and 75% in cost compared to existing works. Then, this memory unit is utilized to design an efficient structure for CAM circuit. The results show that the developed structure for CAM circuit has 0.75 clock cycles, 32 cells, and 0.03 µm2 area, and it has a reduction of 20% in the number of cells, 25% in area, 40% in latency, and 75% in cost compared to existing works.

Hybrid FWPS cooperation algorithm based unmanned aerial vehicle constrained path planning

This paper considers the unmanned aerial vehicle (UAV) global path planning as an optimization problem with multiple constraints and proposes an improved fireworks algorithm (FWA) and particle swarm optimization (PSO) cooperation algorithm to generate an optimal path. The objective function of the UAV flight path is modeled to have the shortest length satisfying strict multiple threat area constraints. The α constrained method using the level comparison strategy is integrated into both FWA and PSO to enhance their superior constraint-handling ability. To increase the population diversity, the whole population is divided to fireworks and particles, which perform search operation in parallel. A new mutation strategy in the fireworks is adopted to avoid falling into the local optimum. Information sharing mechanism between fireworks and particles is established to make the population achieve the excellent global optimization performance. Several numerical simulations are carried out and the results show that our proposed algorithm performs well in obtaining high quality solutions and handling constraints.

Blockchain-based searchable encryption with efficient result verification and fair payment

Data outsourcing has become a popular application. Searchable encryption (SE) can provide data retrievability as well as keep data privacy. In the threat model of SE schemes, the cloud server is usually assumed to be Honest-But-Curious, but this may not be true in reality. The blockchain-based SE makes malicious cloud servers dare not to deviate from the protocol, and search results are validated by miners. However, rational miners would skip the validation step and accept the block directly to save computational resources. This situation is called the Verifier’s Dilemma which will make the blockchain-based SE scheme not real verifiable. Therefore, this paper proposes a verifiable blockchain-based public-key encryption scheme that outsources the verification to the TrueBit network. This design makes our scheme achieves effective verifiability which will minimize the computational cost of miners. Furthermore, our scheme also establishes a fair payment protocol between the multiple data owners and data users. Also, the data owner can revoke the permission of the document he/she shared before. We also present security proofs and analysis of the proposed scheme. Performance evaluation presents the overheads of performing search operations on the cloud server and conducting transactions on smart contracts in Ethereum. The experiment results demonstrate the practicability of our proposed scheme.

Search & rescue operations during interior firefighting: A study into crawling speeds

Firefighters perform search operations during fires in order to rescue people. Full-scale tests to evaluate the effectiveness of the techniques used to search a room have been organized in the absence of fire. 42 search crews completed 7 different set-ups. The crews consisted of Belgian and Dutch firefighters as trained to date. They searched four 12 m2 bedrooms and three 31 m2 office spaces. Searching an empty space was compared to searching a furnished space. The impact of using a hose line was evaluated. 4 min is a good order of magnitude for the search of a bedroom. The time needed increases with increasing surface, but not proportionally. Crews can search a larger area per unit of time in the 31 m2 office than in the 12 m2 bedroom. The average air consumption rate in the breathing apparatus is 68–70 lpm for both rooms. When using a hose, the search becomes longer in time and takes a higher air consumption rate (average: 84 lpm). The decision to take a hose line or not is therefore very important. Crews where the two firefighters are side by side are more effective than crews where they are behind the other.

Formula omitted]-index: A referential index for encrypted genomic databases

Huge DBMSs storing genomic information are being created and engineerized for doing large-scale, comprehensive and in-depth analysis of human beings and their diseases. This paves the way for significant new approaches in medicine, but also poses major challenges for storing, processing and transmitting such big amounts of data in compliance with recent regulations concerning user privacy. We designed and implemented ER-index, a new full-text index in minute space which was optimized for pattern-search on compressed and encrypted genomic data using a reference sequence, and that complements a previous index for reference-free genomics. Thanks to a multi-user and multiple-keys encryption model, a single ER-index can store the sequences related to a large population of individuals so that users may perform search operations directly on compressed data and only on the sequences to which they were granted access.Tests performed of three different computing platforms show that the ER-index get very good compression ratios and search times, outperforming in many cases a reference tool that was proved nearly-optimal in time and space and does not implement encryption.The ER-index C++ source code plus scripts and data to assess the tool performance are available at: https://github.com/EncryptedIndexes/erindex.

Quantum Dimension Reduction for Pattern Recognition in High-Resolution Spatio-Spectral Data

The promises of advanced quantum computing technology have driven research in the simulation of quantum computers on classical hardware, where the feasibility of quantum algorithms for real-world problems can be investigated. In domains such as High Energy Physics (HEP) and Remote Sensing Hyperspectral Imagery, classical computing systems are held back by enormous readouts of high-resolution data. Due to the multi-dimensionality of the readout data, processing and performing pattern recognition operations for this enormous data are both computationally intensive and time-consuming. In this article, we propose a methodology that utilizes Quantum Haar Transform (QHT) and a modified Grover's search algorithm for time-efficient dimension reduction and dynamic pattern recognition in data sets that are characterized by high spatial resolution and high dimensionality. QHT is performed on the data to reduce its dimensionality at preserved spatial locality, while the modified Grover's search algorithm is used to search for dynamically changing multiple patterns in the reduced data set. By performing search operations on the reduced data set, processing overheads are minimized. Moreover, quantum techniques produce results in less time than classical dimension reduction and search methods. The feasibility of the proposed methodology is verified by emulating the quantum algorithms on classical hardware based on field programmable gate arrays (FPGAs). We present designs of the quantum circuits for multi-dimensional QHT and multi-pattern Grover's search. We also present two emulation techniques and the corresponding hardware architectures for this methodology. A high performance reconfigurable computer (HPRC) was used for the experimental evaluation, and high-resolution images were used as the input data set. Analysis of the methods and implications of the experimental results are discussed.

Message-Locked Searchable Encryption: A New Versatile Tool for Secure Cloud Storage

Message-Locked Encryption (MLE) is a useful tool to enable deduplication over encrypted data in cloud storage. It can significantly improve the cloud service quality by eliminating redundancy to save storage resources, and hence user cost, and also providing defense against different types of attacks, such as duplicate faking attack and brute-force attack. A typical MLE scheme only focuses on deduplication. On the other hand, supporting search operations on stored content is another essential requirement for cloud storage. In this article, we present a message-locked searchable encryption (MLSE) scheme in a dual-server setting, which achieves simultaneously the desirable features of supporting deduplication and enabling users to perform search operations over encrypted data. In addition, it supports both multi-keyword and negative keyword searches. We formulate the security notions of MLSE and prove our scheme satisfies all the security requirements. Moreover, we provide an interesting extension of our construction to support Proof of Storage (PoS). Compared with the existing solutions, MLSE achieves better functionalities and efficiency, and hence enables more versatile and efficient cloud storage service.

Shape optimization of shell structure by using Genetic Algorithm (GA) method

Shell structure has been popular for various application in building, aerospace, oil and gas, ship and automobile industry. This type of element is light weight and can be provide great stiffness for large structure. Furthermore, it is very useful for building architecture and appearance. However, currently, construction is not only concerning structural strength but also focusing on building cost and its impact to environment. Finding the balance between structural strength, cost and aesthetic appearance is essentials this nowadays. Hence, construction framework to find the leeway between those parameters is greatly important. This paper will discuss the framework for shape optimization of shell element by using Genetic Algorithm (GA) method. The shape of structure will be developed from Non-Uniform Rationale Bi-Spline (NURBS). Finite element analysis based on Reissner-Mindlin plate theory is utilized to find the element stress within the structure. Genetic algorithm method will evaluate and perform search operation inside the result domain with constraints of structural weight and element stress. The framework then was implemented in numerical example to find its effectiveness on obtaining the most optimum shape of shell structure. Based on the numerical result, it is clear that the proposed frame work is able to reach the convergence to the optimum structure without violating the given constraints.

Efficient Attribute-Based Searchable Encryption on Cloud Storage

Cloud service is very popular for its computing and storage capacity at lower cost. More and more data are being moved to the cloud to reduce storage cost. On the other hand, since the cloud is not fully trustable, in order to protect data privacy against third-parties and even the cloud server, they are usually encrypted by cryptology technology before uploading. However, many operations, such as searching, are hard to perform on encrypted data. To solve this problem, searchable encryption has emerged. Searchable encryption in multi-user setting is much less efficient than in single-user setting. In order to address this problem, we propose a multi-owner to multi-user searchable encryption scheme based on attribute-based encryption. Our scheme keeps data secure in the cloud even against the cloud server. It allows users with appropriate authorizations to perform search operations on encrypted data. In addition, search tokens are generated by users instead of data owners. We prove that token privacy and index privacy are well protected in our scheme. The cloud server and illegimate users are not able to get any useful information about search tokens and ciphertexts. Ciphertexts of our scheme are constant-size, which reduce the time-complexity and bandwidth overhead of our scheme.

Learning motion rules from real data: Neural network for crowd simulation

Abstract This paper addresses the problem of efficiently simulating a believable virtual crowd. Our method is the first one that uses the Neural Network (NN) model to fit behaviors from real crowd data to a crowd simulation. Unlike several rule-based approaches that often result in ‘walking robots’, our model can learn motion rules derived from real data and later simulate human walking motions. Additionally, unlike the existing data-driven crowd simulation methods that have to perform search operations on the bound dataset simultaneously during the simulation, our model directly uses the NN model to generate the proper motion for each crowd member. The proposed method is being tested on various scenarios and compared with state-of-the-art state-action-based methods that are commonly employed in data-driven crowd simulation systems. The results demonstrate a significant increase in speed, as well as better simulation quality.

A Nonvolatile Associative Memory-Based Context-Driven Search Engine Using 90 nm CMOS/MTJ-Hybrid Logic-in-Memory Architecture

This paper presents algorithm, architecture, and fabrication results of a nonvolatile context-driven search engine that reduces energy consumption as well as computational delay compared to classical hardware and software-based approaches. The proposed architecture stores only associations between items from multiple search fields in the form of binary links, and merges repeated field items to reduce the memory requirements and accesses. The fabricated chip achieves <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$13.6\times$</tex> </formula> memory reduction and 89% energy saving compared to a classical field-based approach in hardware, based on content-addressable memory (CAM). Furthermore, it achieves <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$8.6\times$</tex> </formula> reduced number of clock cycles in performing search operations compared to the CAM, and five orders of magnitude reduced number of clock cycles compared to a fabricated and measured ultra low-power CPU-based counterpart running a classical search algorithm in software. The energy consumption of the proposed architecture is on average three orders of magnitude smaller than that of a software-based approach. A magnetic tunnel junction (MTJ)-based logic-in-memory architecture is presented that allows simple routing and eliminates leakage current in standby using 90 nm CMOS/MTJ-hybrid technologies.

A Terrain Referenced UAV Localization Algorithm Using Binary Search Method

This study focuses on localization of Unmanned Aerial Vehicles (UAV) since permanent navigation has vital significance to support position information and to avoid getting lost. Actually, there exist effective aeronautical navigation systems in use. Inertial Navigation System (INS) and Global Positioning System (GPS) are two representatives of the most common systems utilized in traditional aerial vehicles. However, an alternative supporter system for UAVs should be mentioned since INS and GPS have serious deficiencies for UAVs such as accumulated errors and satellite signal loss, respectively. Such handicaps are coped with integrating these systems or exploiting other localization systems. Terrain Referenced Navigation (TRN) could be a good alternative as a supporter mechanism for these main systems. This study aims to localize a UAV accurately by using only the elevation data of the territory in order to simulate a TRN system. Application of the methodology on a real UAV is also considered for the future. Thus assumptions and limitations are designed regarding the constraints of real systems. In order to represent terrain data, Digital Elevation Model (DEM) with original 30 meter-resolution (Eroglu and Yilmaz 2013) and also synthetically generated 10 meter-resolution maps are utilized. The proposed method is based on searching the measured elevation values of the flight within the DEM and makes use of simulation techniques to test the accuracy and the performance. The whole system uses sequences of elevation values with a predefined length (i.e. profile). Mainly, all possible profiles are generated and stored before the flight. We identify, classify and sort profiles to perform search operations in a small subset of the terrain. During the flight, a measured flight profile is searched by the Binary search method (Eroglu 2013) within a small neighborhood of corresponding profile set.

Resource-Constrained Load Balancing Controller for a Parallel Database

This brief documents experimental results using a deterministic dynamic nonlinear system for load balancing, previously reported by Tang et al. in a cluster of computer nodes used for parallel computations in the presence of time delays and resource constraints. While previous publications by the authors have provided theoretical analysis of this load-balancing strategy using an idealized model, and have documented experiments using a simulated database, experimental results using a complete database for DNA profiles are documented here. Evaluation of the proposed load-balancing strategy using an actual database was critical because of several characteristics of the database that cannot be accurately captured using either a simulation model or database, including the variation in times required for the database to perform search operations, the time-varying and task-dependent computational load the database imposes upon each node of the parallel computer, and the time-varying network traffic imposed by both the communication of database search requests and results, mixed with the traffic generated by the load-balancing strategy. Although the load-balancing strategy can be represented in a relatively straightforward manner using mathematics, its implementation is by necessity an approximation to its mathematical description. The reported experimental results serve to validate the superiority of using the controller based on the anticipated work loads to a controller based on local work loads, which has been predicted with experiments using a simulated database and documented in prior publications. The experiments demonstrate the efficacy of the load-balancing strategy using an anticipated pattern of work loads and provide support for scalability of the approach.