Authentication Tree Research Articles

Power data integrity verification method based on chameleon authentication tree algorithm and missing tendency value

Abstract The power system operation and control data are from a wide range of sources. The relevant data acquisition equipment is disturbed by the complex electromagnetic environment on the power system operation and control lines, resulting in data errors and affecting the application and analysis of data. Therefore, a power data integrity verification method based on chameleon authentication tree algorithm and missing trend value is proposed. Get 2D data from different sensors and place it in the space environment. After data conversion, convert heterogeneous data into the same structure, expand the scope of power data acquisition, and conduct power system operation and control node layout and integrity data acquisition; The chameleon authentication tree algorithm is used to deal with the heterogeneous information of the power data, and the true value of the data is determined in the heterogeneous conflict of the power data at the same site; Query the integrity data based on the power system operation and control positioning node, creatively calculate the missing trend value of power data, evaluate the importance of data integrity, obtain the priority of power data integrity verification, and complete the integrity verification of power data. The experimental results show that the optimal clustering number is 9.05, the distribution coefficient is 16.30, the absolute error of validity analysis is 2.80, all test indicators are close to the preset standard, and the trend of the validation curve is close to the trend of the set demand covariance curve. Ensuring the integrity of power data and determining the important indicators of power lines are more conducive to the safe and stable operation of the power data center.

A multi‐parametric machine learning approach using authentication trees for the healthcare industry

AbstractThe Internet of Health Things (IoHT) has grown in importance for developing medical applications with the support of wireless communication systems. IoHT is integrated with many sensors to capture the patients' records and transmits them to hospital centres for analysis and reporting. Controlling and managing health records has been addressed in several ways, however, it is noted that two key research problems for vital communication systems are reliability and reducing data loss. To enhance the sustainability of health applications and effectively use the network infrastructure when transferring sensitive data, this research provides a machine learning approach. Moreover, data collected from the IoHTs are protected and can be securely received for physical process in hospitals using authentication trees. Firstly, the undirected graphs are explored based on the multi‐parametric machine learning approach to minimize the computation overheads and traffic congestion. Secondly, it evaluates the nodes' level behaviour over the heterogeneous traffic load with efficient identification of redundant links. Finally, in‐depth analysis and simulation results have shown that the proposed protocol is more effective than existing approaches for data accuracy and security analysis.

Issues of Clinical Identity Verification for Healthcare Applications over Mobile Terminal Platform

According to recent research, attacks on USIM cards are on the rise. In a 5G setting, attackers can also employ counterfeit USIM cards to circumvent the identity authentication of specified standard applications and steal user information. Under the assumption that the USIM can be replicated, the identity authentication process of common mobile platform applications is investigated. The identity authentication tree is generated by examining the application behavior of user login, password reset, and sensitive operations. We tested 58 typical applications in 7 categories, including social communication and personal health. We found that 29 of them only needed the SMS verification code received by the USIM card to pass the authentication. In response to this problem, it is recommended to enable two-step verification and use USIM anti-counterfeiting methods to complete the verification.

VRBC: A Verifiable Redactable Blockchain with Efficient Query and Integrity Auditing

Driven by various legal obligations and service requirements, the redactable blockchain was introduced to balance the modifiability and immutability of blockchain technology. However, such a blockchain inevitably generates one or even more acceptable versions for the same block data, enabling malicious full nodes to deceive light/new nodes with old data, and even disrupt the consistency of the blockchain ledger. In this paper, we introduce the concept of verifiable redactable blockchain (VRBC) to provide efficient validity verification for on-chain data. To this end, we design a novel authentication data structure, called blockchain authentication tree (BAT), which employs a chameleon hash function and aggregatable vector commitment to bind continuously-appended blocks. Based on this, we propose an efficient VRBC scheme supporting integrity auditing, which not only allows the light nodes to query and validate on-chain data, but also enables new nodes to check the integrity of the blockchain ledger before synchronizing it, effectively avoiding resource waste and security risks caused by invalid queries and ledger synchronization. Furthermore, we introduce some optimized strategies to improve the performance of our scheme and extend it to transaction-level and permissionless VRBC. Finally, we demonstrate the practicability of our scheme through detailed security analysis and visual performance evaluation.

Verifiable data streaming with efficient update for intelligent automation systems

The wide deployment of Internet of Things (IoT) devices enables the controller to continuously collect massive volume data in automation systems, and makes it possible to make intelligent decisions based on machine learning techniques. In fact, data-driven intelligent automation systems have been common in the industrial community. Nevertheless, how to effectively store the collected stream data and ensure their integrity is still challenging. To this end, the notion of verifiable data streaming (VDS) protocol, which enables a client to outsource the stream data to an untrusted server in a verifiable manner, was introduced. However, we argue that existing VDS protocols based on the chameleon authentication tree (CAT) are inefficient in the data update, since the whole CAT must be updated accordingly to avoid acute exposure of chameleon hashing. Thus, they are infeasible for intelligent automation systems that need to frequently update data. In this article, we first introduce a new primitive called double-trapdoor chameleon hash tree (DCHT) based on the double-trapdoor chameleon hash families, where each leaf of DCHT is calculated and fixed by using a double-trapdoor chameleon hash family, making the entire DCHT always unchanged. Furthermore, we propose a novel VDS protocol based on the DCHT. Due to the distinctive properties of the underlying DCHT, the proposed VDS protocol has a constant update cost and more efficient than previous VDS protocols based on CAT. Besides, we prove that the proposed VDS protocol is secure in the standard model.

Identity-based Data Possession Verification Scheme for Multi-Replica Storage in Cloud

In the cloud storage environment, how to efficiently and dynamically complete the integrity verification of multi-user and multi-replica data is a challenging problem. Based on the bilinear mapping signature mechanism and the multi-branch authentication tree, we propose an identity-based integrity verification scheme of multi-replica data for cloud, which solves the problem of large public key storage and management overhead in signature algorithm based on traditional public key cryptosystems. The proof shows that our scheme satisfies the robustness and has the function of protecting privacy. The results of analysis indicate that, compared with the existing similar schemes, our scheme has higher communication efficiency and computing performance.

Cryptoanalysis of an Authenticated Data Structure Scheme With Public Privacy-Preserving Auditing

In this letter, we point out that the privacy-preserving adaptive trapdoor hash authentication tree scheme (published in IEEE TIFS, doi: 10.1109/TIFS.2020.2986879) can be invalidated by an adversarial cloud server: if the outsourced data is arbitrarily modified, the cloud server still can pass the third-party auditor’s auditing.

Verifiable Cloud Data Access: Design, Analysis, and Implementation

In this article, we consider the following cloud storage security problem: A user outsources his/her data to a cloud and later frequently updates the data by adding new data items and deleting existing ones. When the user requests an existing data item from the cloud, the cloud may claim that there is no such data item. This could happen due to cloud management issues, hardware/software failures, hackers, or economic incentives, such as saving cloud’s operation costs. The ability to confirm the data existence/nonexistence when accessing a data item on the dynamic outsourced cloud storage is referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic verifiable data access</i> (DVDA). Existing cloud security research works, including encrypted search and proof of storage (i.e., proof of retrievability and proof of data possession), are not sufficient for addressing dynamic verifiable data access. In this article, we present a solution for the dynamic verifiable data access problem. In order to support data dynamics and verifiability, we enhance a hash authentication tree that is build on top of a hash table with a new semantics. With the enhanced hash authentication tree, the proposed protocol supports adding/deleting data in the outsourced storage while satisfying data access verifiability requirement simultaneously. Our open-source experimental evaluation shows that it only takes less than 0.1 ms to verify a data access on a dataset with 400 items using communication cost around 1.7 KB.

Efficient and Lightweight Data Streaming Authentication in Industrial Control and Automation Systems

The industrial control and automation systems have played an increasingly important role in critical manufacturing processes. In such systems, many Internet of Things devices continuously collect large number of streaming data for real-time processing. Verifiable data streaming (VDS) addresses such authenticity issue for streaming data, but most VDS schemes are not efficient and lightweight, do not support range querying, and cannot be used in practice. To improve the efficiency and achieve a verifiable range query in data streaming, we present here a new primitive, namely, a chameleon authentication tree with prefixes (PCAT), which is extended from the PBTree and chameleon authentication tree. Our scheme is not only lightweight but also supports dynamic expansion and verifiable range query in data streaming, making it more suitable for resource-constrained devices. We separate the PCAT's algorithms into the following phases: initialization, data appending, query, and verification. Our analyses prove that the PCAT satisfies all the security requirements of VDS. Moreover, an efficiency analysis and performance evaluation demonstrate that our scheme not only supports lightweight data streaming authentication but also has high efficiency, which means that the PCAT is easier to apply in the industrial control and automation systems.

Efficient audit protocol supporting virtual nodes in cloud storage

AbstractWith ten years of development, cloud computing has been widely used in more and more field. As a part of cloud computing, cloud storage has earned more and more attentions. It becomes a new trend that users employ cloud storage to outsource their data. Instead of the traditional approach to store data in a local server, users' data cannot be controlled once data outsourcing in cloud storage. At the same time, as the cloud service provider is honest‐but‐curious, it becomes a challenge to make sure that cloud service provider can provide storage service with high quantity. In this paper, we focus on the issue of cloud storage security audit. In particular, we propose a novel public audit scheme, which employs chameleon authentication tree in the third‐party audit model to significantly reduce the huge computation cost of data dynamic operation. Meanwhile, our scheme can effectively prevent the fraud of dishonest cloud service provider, with supporting dynamic data update and batch audit. The experimental results show that the proposed scheme is more secure and efficient compared with the existing schemes.

A chameleon hash authentication tree optimisation audit for data storage security in cloud calculation

In order to improve the security of data storage in cloud calculation, a chameleon hash authentication tree optimisation audit method for data storage security in cloud calculation is proposed. First, an optimised public audit agreement is proposed. By storing homomorphic linear validator for user data on TPA sites, the response size of cloud storage server (CSS) is optimised. At the same time, the quasi-random function is used to optimise the query request to CSS; secondly, the chameleon hash and an improved chameleon authentication tree are used to perform efficient dynamic data updating on client data (cloud calculation) to support block-level updating and fine-grained updating; finally, through thorough security and performance analysis, it is clearly verified that the proposed method is safe and efficient.

An Adaptive Authenticated Data Structure With Privacy-Preserving for Big Data Stream in Cloud

With the rapid development of 5G network, big data and IoT, data in many environments is often continuously and dynamically generated with high growth rates, just like stream. Thus, we call it big data stream, which plays an increasingly important role in all walks of life. However, how to verify its authenticity becomes a challenge when this big data stream in an untrusted environment such as cloud platform, for it faces the problems just like delay-sensitive, unpredictable data size and privacy leaks caused by third-party audits. To solve these problems, we propose a new authenticate data structure named privacy-preserving adaptive trapdoor hash authentication tree (P-ATHAT) by introducing trapdoor hash and BLS signature to the Merkle hash tree. The P-ATHAT scheme realizes real-time verification of data stream and can dynamically expand its structure as the data stream arrives. These characteristics not only shorten the authentication path but also solve the single point failure problem of the conventional authentication trees and enhance the robustness of the scheme. Moreover, we construct a homomorphic verification scheme above tree structure to solve the privacy leakage problem in third-party audit. Finally, security analysis and detailed experimental evaluation are performed on the proposed scheme, both results demonstrate that it is desirable for big data stream authentication and privacy-preserving in practical application.

Research on Integrity Check Method of Cloud Storage Multi-Copy Data Based on Multi-Agent

Aimed at the huge number of files in cloud storage and the low audit efficiency of single-node audit mode, this paper proposes a multi-agent-based cloud storage multi-copy data integrity check scheme. The scheme uses a bilinear mapping method to construct the key generation process and a multi-branch authentication tree to perform multi-copy data signature to implement multi-copy authentication, signature, and verification. It also uses a directed acyclic graph (DAG) to represent the task relationship in the workflow, task and resource allocation based on QoS demand preference settings to schedule multiple tasks. Experimental results show that the proposed scheme has obvious advantages over existing schemes in terms of communication, storage overhead, and audit efficiency, with an average audit efficiency improvement of 20%.

MIMO Cross-Layer Secure Communication Algorithm for Cyber Physical Systems Based on Interference Strategies

In this paper, a detailed analysis of multi-input multi-output (MIMO) cross-layer secure communication algorithms in information-physical systems is investigated employing an interference strategy. A three-stage data-assisted channel estimation method is proposed in this paper for the acquisition of channel state information for complex jamming channels in large-scale MIMO two-layer systems. To implement the data-assisted scheme, assuming that there are no errors and no delay in the system, if the data detection and decoding data sequences are completed at a small cell base station, they are sent to the macro base station via a wired backhaul. Due to the sparsity of the channel at the macro base station after user grouping, a channel estimation algorithm based on optimal block orthogonal matching tracking(s) is proposed in the case where the downlink channel at the macro base station utilizes the decoded uplink data and known training sequences. The simulation results show that the data-assisted method proposed in this paper is effective in improving channel estimation accuracy. A machine learning algorithm is directly used to classify the channel difference or channel matrix to obtain the authentication results. In this paper, the scheme is first simulated using channel data from dynamic communication scenarios, its feasibility is analyzed, and the parameters in the scheme are compared, and the optimal scheme is the bagging tree authentication scheme using a 128-dimensional channel matrix as input. To address the interference problem caused by the dense arrangement of SAPs in heterogeneous networks and the unbalanced network load, large-scale MIMO techniques are introduced to reduce the downlink interference caused by the microcell boundary expansion in heterogeneous networks.

An efficient Minkowski distance-based matching with Merkle hash tree authentication for biometric recognition in cloud computing

Biometric recognition recognizes an identification of an individual which can be obtained with the biological characteristics such as fingerprint, iris, and voice. The challenge of biometric recognition is the matching of fingerprint database with some sort of similarity distance methods. In this paper, Minkowski distance-based matching with Merkle hash tree authentication approach is proposed to overcome the above-mentioned challenges. Merkle hash tree-based encryption algorithm is introduced to improve the privacy concerns, usability and memory durability of the proposed system. This strategy handles security concerns via feature extracted data hashing with the private key. Minutiae feature extraction procedure adequately removes edge-detected features from the sensed data. Notwithstanding the security improvement, the proposed methodology stores just the produced keys for lessening memory stockpiling of cloud framework. Thus, authentication enhancement improves the entire security considerations through the proposed Minkowski distance-based authentication methodology. Finally, to demonstrate the practicality of the scheme, we evaluate the proposed scheme using the MATLAB simulator. A fingerprint dataset named as NIST Special Database 4 (NIST-4) is used for experimental evaluation. The proposed recognition system has achieved superior accuracy of 100% compared to the existing approaches.

ARSENAL: Architecture for Secure Non-Volatile Memories

Whereas data persistence in non-volatile memories (NVMs) enables instant data recovery (IDR) in the face of power/system failures, it also exposes NVMs to data confidentiality and integrity attacks. Counter mode encryption and Merkle Tree authentication are established measures to thwart data confidentiality and integrity attacks, respectively, in NVMs. However, these security mechanisms require high overhead atomic security meta-data updates on every write-back in order to support IDR in NVMs. This increases memory traffic and negatively impacts system performance and memory lifetime. Architecture for Secure Non-Volatile Memories (ARSENAL) is an IDR-preserving, low cost, high performance security solution that protects NVM systems against data confidentiality and integrity attacks. ARSENAL synergistically integrates (i) Smart Writes for Faster Transactions (SWIFT) , a novel technique to reduce the performance overhead of atomic security meta-data updates on every write-back, with (ii) Terminal BMT Updates (TBU) , a novel BMT-consistency-preserving technique, to facilitate IDR in the face of power/system failures. Our evaluations show that on average, ARSENAL improves system performance (measured in IPC) by 2.26 $\times$ (4 $\times$ ), reduces memory traffic overhead by 1.47 $\times$ (1.88 $\times$ ), and improves memory lifetime by 2 $\times$ (3.5 $\times$ ) in comparison to conventional IDR-preserving 64-bit (128-bit) encryption+authentication.

MEAS: memory encryption and authentication secure against side-channel attacks

Memory encryption is used in many devices to protect memory content from attackers with physical access to a device. However, many current memory encryption schemes can be broken using differential power analysis (DPA). In this work, we present Meas—the first Memory Encryption and Authentication Scheme providing security against DPA attacks. The scheme combines ideas from fresh re-keying and authentication trees by storing encryption keys in a tree structure to thwart first-order DPA without the need for DPA-protected cryptographic primitives. Therefore, the design strictly limits the use of every key to encrypt at most two different plaintext values. Meas prevents higher-order DPA without changes to the cipher implementation by using masking of the plaintext values. Meas is applicable to all kinds of memory, e.g., NVM and RAM. For RAM, we give two concrete Meas instances based on the lightweight primitives Ascon, PRINCE, and QARMA. We implement and evaluate both instances on a Zynq XC7Z020 FPGA showing that Meas has memory and performance overhead comparable to existing memory authentication techniques without DPA protection.

Bayesian and Phylogenic Approaches for Studying Relationships among Table Olive Cultivars.

To enhance table olive tree authentication, relationship, and productivity, we consider the analysis of 18 worldwide table olive cultivars (Olea europaea L.) based on morphological, biological, and physicochemical markers analyzed by bioinformatic and biostatistic tools. Accordingly, we assess the relationships between the studied varieties, on the one hand, and the potential productivity-quantitative parameter links on the other hand. The bioinformatic analysis based on the graphical representation of the matrix of Euclidean distances, the principal components analysis, unweighted pair group method with arithmetic mean, and principal coordinate analysis (PCoA) revealed three major clusters which were not correlated with the geographic origin. The statistical analysis based on Kendall's and Spearman correlation coefficients suggests two highly significant associations with both fruit color and pollinization and the productivity character. These results are confirmed by the multiple linear regression prediction models. In fact, based on the coefficient of determination (R 2) value, the best model demonstrated the power of the pollinization on the tree productivity (R 2=0.846). Moreover, the derived directed acyclic graph showed that only two direct influences are detected: effect of tolerance on fruit and stone symmetry on side and effect of tolerance on stone form and oil content on the other side. This work provides better understanding of the diversity available in worldwide table olive cultivars and supplies an important contribution for olive breeding and authenticity.

Dynamic Chameleon Authentication Tree for Verifiable Data Streaming in 5G Networks

Chameleon authentication tree (CAT) is an important authenticated data structure for verifiable data streaming in 5G networks. But the typical CAT cannot support the dynamic scenario very well because it cannot expend freely since its height is fixed. Therefore, we proposed a dynamic CAT (DCAT) with the feature of adaptive expansion. We divided the algorithms of the DCAT with the following phases: setup, append, query, and verification. The DCAT removes the drawbacks of the static CAT. In the setup phase, it is not required for the scale of the tree to be determined, and the scale of the tree can be adaptively expanded during the data-appending phase. Therefore, the DCAT can suit the data stream environment better. During the data querying phase, the average authentication path length has been reduced, which leads to less space requirement and better verification efficiency. Finally, we performed theoretical analysis and drew a comparison between the static CAT and the DCAT in terms of performance. The result indicates that the DCAT provides improvements in the performance of the data-appending, data-querying, and data verification processes.

Hierarchy Merkle tree wireless sensor network architecture

The famous cryptographer Merkle first put forward the idea of Merkle tree authentication. Complete binary tree is used for large amounts of data authentication and signature. In recent years, many scholars use Merkle tree for wireless sensor networks. For the processing of a large number of sensor nodes, Merkle tree can cause high computational complexity, high memory demand, high traffic load, validation delay and a series of practical problems. The Graded Merkle tree is proposed to alleviate some of these issues. However, with the further increase in the number of sensor nodes and expansion of the geographical scope, the wireless sensor networks based on the Graded Merkle tree highlights some new problems, such as scalability, robustness, flexibility, security and so on. In this paper, a modified wireless sensor network architecture based on hierarchical Merkle tree is proposed to effectively solve the above problems.