The rapid growth of cloud computing has enabled collaborative data analysis across distributed systems, yet ensuring privacy under stringent regulations remains a critical challenge. Data Collaboration Analysis (DCA) facilitates efficient analysis in such environments but struggles to balance robust privacy with model accuracy under strict constraints . Traditional Differential Privacy (DP) methods often degrade performance due to excessive noise (e.g.,$\epsilon = 2$). We propose SSDP-DCA, a novel framework that enhances DCA by integrating DP with privacy amplification techniques, leveraging the Shuffle Model for anonymization andpackage Subsampling (applied post-shuffle)for privacy amplification. Experimental results demonstrate SSDP-DCA's superiority over baselines, achieving high utility under a fixed global privacy budget, thus offering a robust solution for secure federated learning and healthcare applications.

Blockchain-based information storage and retrieval systems face significant challenges in achieving efficiency, privacy, and auditability when operating across heterogeneous blockchain platforms. Existing solutions often struggle to balance these requirements, particularly in cross-chain environments involving both public and consortium blockchains. This paper proposes a novel framework that leverages cross-chain technology to address these limitations. The framework integrates multi-party threshold cross-chain consensus to optimize verification efficiency and reduce the computational burden on trusted nodes. To ensure privacy-preserving information querying and retrieval, advanced cryptographic techniques are employed. Additionally, a dedicated auditor set within the consortium blockchain is introduced to detect malicious behavior and enforce regulatory compliance. Comparative evaluations demonstrate that the proposed framework outperforms existing methods in terms of privacy protection, efficiency, and auditability. Experimental results on Hyperledger Fabric demonstrate significant improvements in throughput, achieving at least 20 Transactions Per Second (TPS), along with latency below 3.5 seconds and 300MB memory utilization under standard PC configurations. These findings validate the framework's practical viability for secure and efficient cross-chain information retrieval while maintaining superior performance compared to existing solutions.

NFT is a prominent blockchain technology product that seeps into society after cryptocurrency. Most NFT structures use URL(s) to access external off-chain storage, where the associated asset’s metadata information or the asset itself is stored. This practice is commonly done due to its simplicity, gas cost consideration, or even the possibility of doing on-chain storage. However, questions could be raised for this mainstream NFT structure: What happens if the URL cannot be accessed? Is there any way to verify that an asset is an NFT, suppose the URL is broken? Issues regarding the linkability between an NFT and its asset and the impracticality of NFT verification are rarely highlighted, exposing users to risks such as purchasing an "empty NFT," potentially resulting in growing distrust toward NFT. This paper aims to answer these concerns by proposing a method that preserves and enhances NFTs to their respective asset’s linkability while acting as an NFT verification tool. The method involves registering, mapping, and storing the asset’s hash value with its related NFT information in a resolver smart contract while enabling verification for NFT minted in different blockchain networks.

This study explores how user interaction with content relates to video popularity on Bilibili, a video-sharing platform. We focus on two primary forms of interaction, danmaku and traditional comment sections, to examine how different content-level characteristics may influence user engagement outcomes. Using an exploratory content analysis framework, we assess six dimensions of potentially controversial expression, quantified through the Perspective API and cross-validated with ChatGPT-4o to address language limitations. A composite popularity index is constructed by integrating views, likes, favorites, danmaku density, and comment density. Hierarchical regression results suggest that interaction volume is the strongest predictor of video popularity, while content-related signals contribute additional explanatory power. Specifically, the fluctuation of overall toxicity in danmaku is positively associated with video popularity, whereas fluctuations in identity attacks show a negative association. In traditional comments, higher average levels of identity attacks and insults are significantly negatively correlated with video popularity. This study contributes by: (1) introducing a multidimensional exploratory framework to assess content-level expression in user interactions; (2) identifying distinct patterns between danmaku and comments in their associations with popularity; and (3) offering implications for content moderation and interaction design in video-sharing environments.

To detect malicious URLs more timely, machine learning based malicious URL detection methods have replaced traditional blacklist methods. These studies aim to improve the accuracy and speed of detection from various aspects such as URL segmentation, URL embedding methods, machine learning models, etc. However, the security issues inherent in these machine learning based malicious URL detection methods have been overlooked. Adversarial example attacks are one of the security issues faced by machine learning based malicious URL detectors. In this paper, we proposed a new adversarial example attack method against malicious URL detection based on machine learning and it has better performance than existing methods. Besides, we compared the robustness of different URL embedding methods and machine learning models with our attack methods and existing attack methods. At last, we analyzed the reasons why our proposed method performs better and the reasons why the context-considered embedding method has high resistance to adversarial example attacks.

The rapid growth and widespread application of Internet of Things (IoT) devices have resulted in a significant in-crease in the demand for data transmission. However, IoT devices often operate in unstable environments and are constrained by their limited computing and communication capabilities, which makes stable and reliable data transmission a major challenge. Due to the diversity and complexity of IoT platforms, existing systems struggle to provide flexible and scalable solutions. To address these issues, we propose a system based on blockchain and smart contracts that enhances data transmission redundancy by integrating external collaborative devices, thereby ensuring both transmission stability and security. The proposed system achieves efficient authentication through the use of certificates and smart contracts, while also promoting the participation of temporary devices through incentive mechanisms. Compared to existing methods, our system not only enhances security but also optimizes resource utilization and reduces operational costs.

The global outbreak of new coronavirus infections has increased the demand for smartphone-based contact tracing. In particular, much research has been conducted on contact tracing using blockchain, which is decentralized and hard to tamper with data. However, contact tracing requires the centralized collection of information for tracing users, and this essence remains the same even when using blockchain. In this study, we propose a blockchain-based contact tracing method that mitigates the centralized information collection and considers user privacy so that the administrator cannot trace the information. In this scheme, only the application knows the wallet address corresponding to the user, so even the trust authority cannot determine the user's identity from the wallet address. This method is capable of notifying users who may be witnesses at a crime scene by using smart contracts. Users can also prove their innocence if suspected of a crime. Furthermore, we will demonstrate the effectiveness of this method by conducting a demonstration experiment using an Ethereum testnet and two smartphones.

One blockchain technological update thus far includes smart contract functionality implemented in Ethereum. One of its use cases, the ERC721 Non-Fungible Token Standard (NFT), has gained attention from current industries. NFTs demonstrated digital content transactions with guaranteed uniqueness using unchangeable timestamp on the blockchain. However, the functional limitations of NFT assurance are rarely in focus, and contrary to excessive NFT user expectations, security risks that must be addressed include complex issues. One such risk is NFT fraudulent trading using forged content. Specifically, hard forks in blockchains are extremely important triggers for attacks, as they allow the exact same NFT to be operated on multiple chains. This could depreciate NFT value, and presents a risk that must be addressed to ensure sound NFT transactions. In this study, NFT operational issues in the case of a blockchain fork are summarized, and a cross-referencing scheme using network identities is proposed to prevent the impact of a hard fork.

In recent years, decentralized global ID systems have attracted significant attention. The concept of self-sovereign identity was established to realize a decentralized global ID, which is achieved by verifiable credentials and decentralized identifiers. However, there is a problem in using such decentralized global IDs for various remote services from the viewpoint of flexibility. In this paper, we propose a system which can give an "attribute" to global IDs dynamically by using only the basic function of a blockchain (i.e., using cryptocurrency transfer only). The main idea of our scheme is that an "attribute token" is assigned to global IDs or removed from global IDs using the token transmission function in a blockchain. Thus, we can set the credibility level and decentralization level of global IDs adaptively, and thus, we manage global IDs securely and flexibly. Furthermore, we conduct feasibility experiments with the proposed system and realize an attribute token assignment in Bitcoin and Ethereum cryptocurrencies. This result shows that our system can use almost all blockchain systems having a token transmission function.

In order to solve the shortcomings of using blacklist method to detect malicious URLs, such as slow update speed, the research of using machine learning to detect malignant URLs are increasing. These researches have proposed their own methods and obtained great accuracy, but the summary research on malicious URLs detection is insufficient. In this paper, we propose a three-step framework for malicious URLs detection and we overview 14 related works by our three-step framework and find that almost all researches of malicious URLs detection using machine learning can be classified by three-step framework. We evaluate some machine learning models and context-considering methods and their suitability by our three-step framework. According to the results, we verify the importance of considering context and find that context-considering embedding methods are more important and the malicious URLs detection accuracy improved with context-considering methods.

In recent years, machine learning technology has been extensively utilized, leading to increased attention to the security of AI systems. In the field of image recognition, an attack technique called clean-label backdoor attack has been widely studied, and it is more difficult to detect than general backdoor attacks because data labels do not change when tampering with poisoning data during model training. However, there remains a lack of research on malware detection systems. Some of the current work is under the white-box assumption that requires knowledge of machine learning-based models which can be advantageous for attackers. In this study, we focus on clean-label backdoor attacks in malware detection systems and propose a new clean-label backdoor attack under the black-box assumption that does not require knowledge of machine learning-based models, which is riskier. The experimental evaluation of the proposed attack method shows that the attack success rate is up to 80.50% when the poisoning rate is 14.00%, demonstrating the effectiveness of the proposed attack method. In addition, we experimentally evaluated the effectiveness of the dimensionality reduction techniques in preventing clean-label backdoor attacks, and showed that it can reduce the attack success rate by 76.00%.

With recent technological developments, it has become natural for personal computers and Internet of Things(IoT) devices, such as smartphones and tablets, to remain constantly connected to the Internet. Malicious attackers are known to abuse malware to achieve their nefarious purposes, necessitating the implementation of defense systems as protection. Methods such as machine learning-based techniques, which have been utilized with great success in various fields such as image recognition and processing, and voice recognition, are used to prevent cyberattacks caused by malware. However, several adversarial attack methods have been proposed in recent years to induce malfunctions in machine learning-based models. In this study, we focus on malware detection field and treat the aforementioned issue from the perspectives of both attackers and defenders; subsequently, we propose a novel adversarial attack method, named IMAGE_RESOURCE attack, and a robust malware detection model, respectively, using dimension reduction and machine learning techniques. The robustness of the proposed model is evaluated using portable execution (PE) surface information obtained from the FFRI Dataset 2018. During robustness evaluation, distances (e.g., Euclidean distance) between the malware and benign files are measured, and the effectiveness of IMAGE_RESOURCE attack is estimated. Thus, we establish the effectiveness and superiority of the proposed model in terms of detection accuracy and robustness.

In recent years, increasingly many companies have entered the pay-as-you-go business because it has become easier to monitor services constantly due to the development and increase in the number of Internet of Things (IoT) devices. Research is in progress to introduce cryptographic assets into the payment, but if a private key is stolen, the cryptographic assets associated with it can be stolen. To address this issue, this paper proposes a secure automated payment system using contract wallets. This method ensures the security of cryptographic assets even if the private key is stolen. Secure automated payments are enabled by issuing transactions from IoT devices and using internal transactions to link contract wallets and smart contracts that handle the payment of cryptographic assets. Furthermore, the effectiveness of the proposed system is demonstrated on the Ethereum blockchain as a proof of concept, and the cost of gas is measured.

Secret sharing scheme has been applied commonly in distributed storage for Big Data. It is a method for protecting outsourced data against data leakage and for securing key management systems. The secret is distributed among a group of participants where each participant holds a share of the secret. The secret can be only reconstructed when a sufficient number of shares are reconstituted. Although many secret sharing schemes have been proposed, they are still inefficient in terms of share size, communication cost and storage cost; and also lack robustness in terms of exact-share repair. In this paper, for the first time, we propose a new secret sharing scheme based on Slepian-Wolf coding. Our scheme can achieve an optimal share size utilizing the simple binning idea of the coding. It also enhances the exact-share repair feature whereby the shares remain consistent even if they are corrupted. We show, through experiments, how our scheme can significantly reduce the communication and storage costs while still being able to support direct share repair leveraging lightweight exclusive-OR (XOR) operation for fast computation.

This paper introduces a new capability for group signatures called message-dependent opening. It is intended to weaken the high trust placed on the opener; i.e., no anonymity against the opener is provided by an ordinary group signature scheme. In a group signature scheme with message-dependent opening (GS-MDO), in addition to the opener, we set up an admitter that is not able to extract any user’s identity but admits the opener to open signatures by specifying messages where signatures on the specified messages will be opened by the opener. The opener cannot extract the signer’s identity from any signature whose corresponding message is not specified by the admitter. This paper presents formal definitions of GS-MDO and proposes a generic construction of it from identity-based encryption and adaptive non-interactive zero-knowledge proofs. Moreover, we propose two specific constructions, one in the standard model and one in the random oracle model. Our scheme in the standard model is an instantiation of our generic construction but the message-dependent opening property is bounded. In contrast, our scheme in the random oracle model is not a direct instantiation of our generic construction but is optimized to increase efficiency and achieves the unbounded message-dependent opening property. Furthermore, we also demonstrate that GS-MDO implies identity-based encryption, thus implying that identity-based encryption is essential for designing GS-MDO schemes.

As data leakage accidents occur every year, the security of confidential information is becoming increasingly important. Remote Access Trojans (RAT), a kind of spyware, are used to invade the PC of a victim through targeted attacks. After the intrusion, the attacker can monitor and control the victim's PC remotely, to wait for an opportunity to steal the confidential information. Since it is hard to prevent the intrusion of RATs completely, preventing confidential information being leaked back to the attacker is the main issue. Various existing approaches introduce different network behaviors of RAT to construct detection systems. Unfortunately, two challenges remain: one is to detect RAT sessions as early as possible, the other is to remain a high accuracy to detect RAT sessions, while there exist normal applications whose traffic behave similarly to RATs. In this paper, we propose a novel approach to detect RAT sessions in the early stage of communication. To differentiate network behaviors between normal applications and RAT, we extract the features from the traffic of a short period of time at the beginning. Afterward, we use machine learning techniques to train the detection model, then evaluate it by K-Fold cross-validation. The results show that our approach is able to detect RAT sessions with a high accuracy. In particular, our approach achieves over 96% accuracy together with the FNR of 10% by Random Forest algorithm, which means that our approach is valid to detect RAT sessions in the early stage of communication.

Summary Availability is very important for long‐term use of wireless sensor networks (WSNs), assuming the presence of an attacker. It is thus important to achieve secure communication among WSNs even if some sensor nodes are compromised. Self‐healing WSNs possess the feature that a network automaticallyself‐healsafter node‐capture attacks in order to achieve availability. The self‐healing means that the ratio of compromised links decreases with time, even if the attacker corrupts sensor nodes of the network. In this paper, three kinds of self‐healing schemes for WSNs are described, a polynomial‐based self‐healing scheme, a simple random key pre‐distribution scheme with self‐healing, and a proactive co‐operative link self‐healing scheme. Our contributions are the self‐healing schemes with security evaluation, in which we conduct analytical evaluation and a simulation experiment of our schemes, and results obtained from both analysis and simulations indicate that our schemes are effective in self‐healing. Furthermore, comparing three schemes, we clarify each difference and discuss optimal scheme under each different environments. © 2015 The Authors.Concurrency and Computation: Practice and ExperiencePublished by John Wiley & Sons Ltd.

Abstract For searching keywords against encrypted data, public key encryption scheme with keyword search (PEKS), and its extension secure‐channel free PEKS (SCF‐PEKS), has been proposed. In this paper, we extend the security of SCF‐PEKS, calling it adaptive SCF‐PEKS, wherein an adversary (modeled as a “malicious‐but‐legitimate” receiver) is allowed to issue test queries adaptively. We show that adaptive SCF‐PEKS can be generically constructed by anonymous identity‐based encryption only. That is, SCF‐PEKS can be constructed without any additional cryptographic primitive when compared with the Abdallaet al.PEKS construction (J. Cryptology 2008), even though adaptive SCF‐PEKS requires additional functionalities. We also propose other adaptive SCF‐PEKS construction, which is not fully generic but is efficient compared with the first one. Finally, we instantiate an adaptive SCF‐PEKS scheme (via our second construction) that achieves a similar level of efficiency for the costs of the test procedure and encryption, compared with the (non‐adaptive secure) SCF‐PEKS scheme by Fanget al.(CANS2009). Copyright © 2014 John Wiley & Sons, Ltd.

Information is increasing quickly, database owners have tendency to outsource their data to an external service provider called Cloud Computing. Using Cloud, clients can remotely store their data without burden of local data storage and maintenance. However, such service provider is untrusted, therefore there are some challenges in data security: integrity, availability and confidentiality. Since integrity and availability are prerequisite conditions of the existence of a system, we mainly focus on them rather than confidentiality. To ensure integrity and availability, researchers have proposed network coding-based POR (Proof of Retrievability) schemes that enable the servers to demonstrate whether the data is retrievable or not. However, most of network coding-based POR schemes are inefficient in data checking and also cannot prevent a common attack in POR: small corruption attack. In this paper, we propose a new network coding-based POR scheme using dispersal code in order to reduce cost in checking phase and also to prevent small corruption attack.

In this paper, we propose a simple random key pre-distribution (RKP) scheme with self-healing for multiphase wireless sensor networks (WSNs) without even lightweight operations such as a hash function, in which the link compromised by node-capture attack in a WSN automatically self-heals with time. Our scheme can enhance the resiliency of existing RKP schemes by only updating the key pool of a server with time. As a result, processing of each sensor is still the same as in the RKP schemes. More interestingly, round synchronization is not necessary to be implemented in a node. Our contribution is the simple scheme with security evaluation, in which we conduct analytical evaluation and a simulation experiment of our scheme, and results obtained from both analysis and simulations indicate that our scheme is effective in self-healing.

In this paper, we propose a strongly resilient polynomial-based random key pre-distribution scheme for multiphase wireless sensor networks (RPoK): a private sub-key is not directly stored in each sensor node by applying the polynomial-based scheme to the RoK scheme. Such a polynomial is linearly transformed using forward and backward keys in order to achieve the forward and backward security of polynomials. As a result, our scheme achieves a large reduction of the ratio of compromised links by enhancing the security of the previous RoK scheme. The results obtained analytically and by simulations show that our scheme can dramatically improve the ratio of compromised links compared with the RoK scheme.

Recently, cryptographic schemes based on the user's attributes have been proposed. An attribute-based group signature (ABGS) scheme is a kind of group signature schemes, where a user with a set of attributes can prove anonymously whether she has these attributes or not. An access tree is applied to express the relationships among some attributes. However, previous schemes do not provide the changing an access tree. In this paper, we propose a dynamic ABGS scheme that enables an access tree to be changed. Our ABGS is efficient in that re-issuing of the attribute certificate previously issued for each user is not necessary. Moreover, calculations depending on the number of attributes are calculated on the domain of a pairing. Therefore, the number of calculations in a pairing does not depend on the number of attributes associated with a signature. Finally, we discuss how our ABGS can be applied to an anonymous survey for collection of attribute statistics.

In IEEE ISI 2008, an anonymous attribute authentication scheme has been proposed using a self-blindable certificate scheme. This scheme enables the anonymity and certificate revocation. A Certificate Revocation List (CRL) is used in the revocation check. Even if an attacker can obtain a CRL, the attacker cannot execute the revocation check. This means that this scheme enables the designated revocation. However, this scheme is not secure, namely, a user can make a forged proof using a public value. In this paper, we propose a certificate revocable anonymous authentication scheme with designated verifier. Our scheme enables the anonymity and certificate revocation. Moreover, our scheme enables a designated verification and revocation.

Virtual machine monitors (VMMs), including hypervisors, are a popular platform for implementing various security functionalities. However, traditional VMMs require numerous components for providing virtual hardware devices and for sharing and protecting system resources among virtual machines (VMs), enlarging the code size of and reducing the reliability of the VMMs.This paper introduces a hypervisor architecture, called parapass-through, designed to minimize the code size of hypervisors by allowing most of the I/O access from the guest operating system (OS) to pass-through the hypervisor, while the minimum access necessary to implement security functionalities is completely mediated by the hypervisor. This architecture uses device drivers of the guest OS to handle devices, thereby reducing the size of components in the hypervisor to provide virtual devices. This architecture also allows to run only single VM on it, eliminating the components for sharing and protecting system resources among VMs.We implemented a hypervisor called BitVisor and a parapass-through driver for enforcing storage encryption of ATA devices based on the parapass-through architecture. The experimental result reveals that the hypervisor and ATA driver require approximately 20 kilo lines of code (KLOC) and 1.4 KLOC respectively.

In recent years, as the data processed by governmental or commercial organizations increases, cases involving information leak have risen. It is difficult to control information on many distributed end-point computers using conventional security mechanisms. Therefore, we have been proposed a novel secure VMM (Virtual Machine Monitor) architecture which is used as a foundation of security policy enforcement on distributed computers. This paper especially introduces Role-based Access Control (RBAC) to theID management framework in a secure VMM system. Our proposal will reduce costs for distributed policies updates. Proposed RBAC mechanism employs attribute certificates (ACs) to handle user’s roles. This paper shows design and prototype implementation based on PKI-based ID card and proven open source VMM software, QEMU.