Search

Summary: In the current landscape, the rapid expansion of the internet has brought about a corresponding surge in the number of data consumers. As user volume and diversity have escalated, the shift from conventional, uniform networks to Heterogeneous Networks (HetNets) has emerged. HetNets are designed with a primary objective: enhancing Quality of Service (QoS) standards for users. In the context of HetNets facilitated by Unmanned Aerial Vehicles (UAVs), a substantial influx of users and devices is observed. Within this multifaceted environment, the potential for malicious intruder nodes to efficiently execute and propagate harmful actions across the network is a distinct concern. Consequently, the entirety of network communication becomes susceptible to a multitude of security threats. To address these vulnerabilities and safeguard communication, the Functional Encryption (FE) technique is employed. FE empowers the protection of data against intrusion attacks. This paper presents a comprehensive methodology for implementing FE within UAV‐integrated HetNets, executed in two sequential phases. The initial phase secures communication between User Equipment (UE) and Micro Base Station (MBS), followed by the second phase, which focuses on securing communication among MBS and UAV. The viability of the proposed approach is substantiated through validation using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The validation process involves the development of High‐Level Protocol Specification Language (HLPSL) codes. The successful security validation outcome underscores the capacity of the proposed methodology to provide the intended security measures and robustness to the network environment. [ABSTRACT FROM AUTHOR]

This paper presents the first functional encryption (FE) scheme for the attribute-weighted sum functionality that supports the uniform model of computation. In such an FE scheme, encryption takes as input a pair of attributes (x, z) where x is public and z is private. A secret key corresponds to some weight function f, and decryption recovers the weighted sum f(x)z. In our scheme, both the public and private attributes can be of arbitrary polynomial lengths that are not fixed at system setup. The weight functions are modelled as Logspace Turing machines . Prior schemes could only support non-uniform Logspace. The proposed scheme is proven adaptively simulation secure under the well-studied symmetric external Diffie–Hellman assumption against an arbitrary polynomial number of secret key queries both before and after the challenge ciphertext. This is the best possible security notion that could be achieved for FE. On the technical side, our contributions lie in extending the techniques of Lin and Luo [EUROCRYPT 2020] devised for indistinguishability-based payload hiding attribute-based encryption for uniform Logspace access policies and the "three-slot reduction" technique for simulation-secure attribute-hiding FE for non-uniform Logspace devised by Datta and Pal [ASIACRYPT 2021] to the context of simulation-secure attribute-hiding FE for uniform Logspace. [ABSTRACT FROM AUTHOR]

With the advancement of smart power plants, electricity consumers are transitioning from a passive electricity consumption model to an intelligent consumption model. However, distributed power equipment and the vast amounts of electricity consumption data they generate pose significant information security risks. Traditional access control mechanisms cannot meet the security needs of distributed smart power plants. To address this issue, this paper introduces a distributed smart power plant access control mechanism based on blockchain and ciphertext updatable functional encryption. Firstly, the decentralized and tamper-proof features of blockchain technology ensure the integrity, transparency, and security of data transmission, effectively preventing data tampering and leakage. Secondly, by incorporating ciphertext updatable functional encryption, we guarantee data confidentiality and achieve fine-grained access control over electricity consumption data. This allows users to independently and precisely control data sharing and access. Furthermore, by deploying smart contracts with trust mechanisms on the blockchain, automated management of storage and access is achieved. Through a comprehensive evaluation of trust levels, trust values, and honesty, the system not only enhances its efficiency but also ensures that only legitimate entities can access data resources, thus preserving the utility of electricity consumption data. In addition, the adoption of decentralized unique identifiers on the blockchain for user authentication and authorization strengthens user privacy protection and data sovereignty. Lastly, experimental results demonstrate that this mechanism, while ensuring security, maintains a high level of efficiency, meeting the complex security requirements of distributed smart power plants. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Cryptologic Research (2097-4116) is the property of Editorial Board of Journal of Cryptologic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Graph neural network(GNN) has become one of the research field in the new wave of technological revolution and industrial transformation. High-performance GNN makes full use of structural information, aggregates neighbor information through a message passing mechanism, and finally successfully updates node embeddings, which effectively overcomes the limitations of traditional neural networks. However, in practice, an adversary can use the embedding of nodes to infer information about the architecture and parameters of the GNN model, which poses a potential threat to privacy. To solve this problem, we propose a privacy-preserving vertical federated graph neural network model training framework based on split learning (SVFGNN), an efficient secure gradient computation framework for vertical federated graph neural network models, which can be extended to existing GNN models. Specifically, in the forward propagation process, in order to reduce the client to perform a large number of calculation locally, we employ split learning for device-cloud collaborative training, and apply function-hiding multi-input function encryption technology to protect the parameters uploaded during model training. This solution not only ensures the security of model data and user privacy, but also creates high-precision models and supports lossless training. At the same time, in the process of backward propagation, in order to accurately predict the classification problem of nodes, we need to safely transmit errors and respond to client requests in a timely manner. To achieve this goal, we adopt hybrid encryption technology to realize cloud ciphertext data sharing, which eliminates point-to-point communication between various parties and significantly reduces communication overhead during model training. Compared to state-of-the-art works, our communication overhead scales linearly with the number of clients. Through the optimization of this research, we can better deal with the risk of privacy leakage, and provide a feasible security guarantee for the practical application of vertical federated graph neural network, and promote the development and progress of this field. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Cryptologic Research (2095-7025) is the property of Editorial Board of Journal of Cryptologic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Federated learning, a decentralized paradigm, offers the potential to train models across multiple devices while preserving data privacy. However, challenges such as malicious actors and model parameter leakage have raised concerns. To tackle these issues, we introduce a game-theoretic, trustworthy anti-collusion federated learning scheme, which combines game-theoretic techniques and rational trust models with functional encryption and smart contracts for enhanced security. Our empirical evaluations, using datasets like MNIST, CIFAR-10, and Fashion MNIST, underscore the influence of data distribution on performance, with IID setups outshining non-IID ones. The proposed scheme also showcased scalability across diverse client counts, adaptability to various tasks, and heightened security through game theory. A critical observation was the trade-off between privacy measures and optimal model performance. Overall, our findings highlight the scheme's capability to bolster federated learning's robustness and security. [ABSTRACT FROM AUTHOR]

In the context of functional encryption (FE), a weak security notion called selective security, which enforces the adversary to complete a challenge prior to seeing the system parameters, is used to argue in favor of the security of proposed cryptosystems. These results are often considered as an intermediate step to design adaptively secure cryptosystems. In fact, selectively secure FE schemes play a role of more than an intermediate step in many cases. If we restrict our attention to group-based constructions, it is not surprising to find several selectively secure FE schemes such that no successful adaptive adversary is found yet and/or it is also believed that no adaptive adversary will be found in practice even in the future. In this paper, we aim at clarifying these beliefs rigorously in the ideal model, called generic group model (GGM). First, we refine the definitions of the GGM and the security notions for FE scheme for clarification. Second, we formalize a group-based FE scheme with some conditions and then show that for any group-based FE scheme satisfying these conditions we can reduce from its selective security in the standard model to adaptive security in the GGM, in particular, regardless of the functionality of FE schemes. Our reduction is applicable to many existing selectively secure FE schemes with various functionalities, e.g., the FE scheme for quadratic functions of Baltico et al. (CRYPTO, 2017), the predicate encryption scheme of Katz et al. (J Cryptol in 26:191–224, 2013), and Boneh and Boyen's identity-based encryption scheme (EUROCRYPT 2004). [ABSTRACT FROM AUTHOR]

This paper presents the first adaptively simulation secure functional encryption (FE) schemes for attribute-weighted sums. In the proposed FE schemes, attributes are viewed as vectors and weight functions are arithmetic branching programs (ABP). We present two schemes with varying parameters and levels of adaptive simulation security. (a) We first present a one-slot scheme supporting a bounded number of ciphertext queries and an arbitrary polynomial number of secret key queries both before and after the ciphertext queries. This is the best possible level of security one can achieve in the adaptive simulation-based framework. The scheme also achieves indistinguishability-based adaptive security against an unbounded number of ciphertext and secret key queries. (b) Next, bootstrapping from the one-slot scheme, we present an unbounded-slot scheme that can support a bounded number of ciphertext and pre-ciphertext secret key queries while supporting an a-priori unbounded number of post-ciphertext secret key queries. Both schemes enjoy ciphertexts that do not grow with the number of appearances of the attributes within the weight functions. The schemes are built upon prime-order asymmetric bilinear groups and the security is derived under the standard (bilateral) k-Linear (k-Lin) assumption. Our work resolves an open problem posed by Abdalla et al (In: CRYPTO, Springer, New York, 2020), where they presented an unbounded-slot FE scheme for attribute-weighted sum achieving only semi-adaptive simulation security. Technically, we extend the recent adaptive security framework of Lin and Luo (In: EUROCRYPT, Springer, New York, 2020), devised to achieve compact ciphertexts in the context of indistinguishability-based payload-hiding security, to the setting of simulation-based adaptive attribute-hiding security. [ABSTRACT FROM AUTHOR]

A multi-client functional encryption (MCFE ) scheme [Goldwasser–Gordon–Goyal 2014] for set intersection is a cryptographic primitive that enables an evaluator to learn the intersection from all sets of a predetermined number of clients, without need to learn the plaintext set of each individual client. Using these schemes, it is impossible to compute the set intersections from arbitrary subsets of clients, and thus, this constraint limits the range of its applications. To provide such a possibility, we redefine the syntax and security notions of MCFE schemes, and introduce flexible multi-client functional encryption (FMCFE ) schemes. We extend the aIND security of MCFE schemes to aIND security of FMCFE schemes in a straightforward way. For a universal set with polynomial size in security parameter, we propose an FMCFE construction for achieving aIND security. Our construction computes set intersection for n clients that each holds a set with m elements, in time O (n m) . We also prove the security of our construction under DDH1 that it is a variant of the symmetric external Diffie–Hellman (SXDH) assumption. [ABSTRACT FROM AUTHOR]

In Internet of Things (IoT) systems in which a large number of IoT devices are connected to each other and to third-party servers, it is crucial to verify whether each device operates appropriately. Although anomaly detection can help with this verification, individual devices cannot afford this process because of resource constraints. Therefore, it is reasonable to outsource anomaly detection to servers; however, sharing device state information with outside servers may raise privacy concerns. In this paper, we propose a method to compute the L p distance privately for even p > 2 using inner product functional encryption and we use this method to compute an advanced metric, namely p-powered error, for anomaly detection in a privacy-preserving manner. We demonstrate implementations on both a desktop computer and Raspberry Pi device to confirm the feasibility of our method. The experimental results demonstrate that the proposed method is sufficiently efficient for use in real-world IoT devices. Finally, we suggest two possible applications of the proposed computation method for L p distance for privacy-preserving anomaly detection, namely smart building management and remote device diagnosis. [ABSTRACT FROM AUTHOR]

Network marketing is a trading technique that provides companies with the opportunity to increase sales. With the increasing number of Internet-based purchases, several threats are increasingly observed in this field, such as user privacy violations, company owner (CO) fraud, the changing of sold products’ information, and the scalability of selling networks. This study presents the concept of a blockchain-based market called ACR-MLM that functions based on the multi-level marketing (MLM) model, through which registered users receive anonymous and confidential rewards for their own and their subgroups’ sales. Applying a public blockchain as the ACR-MLM framework’s infrastructure solves existing problems in MLM-based markets, such as CO fraud (against the government or its users), user privacy violations (obtaining their real names or subgroup users), and scalability (when vast numbers of users have been registered). To provide confidentiality and scalability to the ACR-MLM framework, hierarchical identity-based encryption (HIBE) was applied with a functional encryption (FE) scheme. Finally, the security of ACR-MLM is analyzed using the random oracle (RO) model and then evaluated.

Functional encryption (FE) is a new paradigm of public key encryption that can control the exposed information of plaintexts by supporting computation on encrypted data. In this paper, we propose efficient multi-client FE (MCFE) schemes that compute the set intersection of ciphertexts generated by two clients. First, we propose an MCFE scheme that calculates the set intersection cardinality (MCFE-SIC) and prove its static security under dynamic assumptions. Next, we extend our MCFE-SIC scheme to an MCFE scheme for set intersection (MCFE-SI) and prove its static security under dynamic assumptions. The decryption algorithm of our MCFE-SI scheme is more efficient than the existing MCFE-SI scheme because it requires fewer pairing operations to calculate the intersection of two clients. Finally, we propose a decentralized MCFE scheme for set intersection (DMCFE-SI) that decentralizes the generation of function keys. Our MCFE schemes can be effectively applied to a privacy-preserving contact tracing system to prevent the spread of recent infectious diseases. [ABSTRACT FROM AUTHOR]

Indistinguishability obfuscation (IO) is a tremendous notion, powerful enough to give rise to almost any known cryptographic object. Prior candidate IO constructions were based on specific assumptions on algebraic objects called multi-linear graded encodings. We present a generic construction of indistinguishability obfuscation from public-key functional encryption with succinct encryption circuits and subexponential security. This shows the equivalence of indistinguishability obfuscation and public-key functional encryption, a primitive that has previously seemed to be much weaker, lacking the power and the staggering range of applications of indistinguishability obfuscation. Our main construction can be based on functional encryption schemes that support a single functional key, and where the encryption circuit grows sub-linearly in the circuit-size of the function. We further show that sublinear succinctness in circuit-size for single-key schemes can be traded with sublinear succinctness in the number of keys (also known as the collusion-size) for multi-key schemes. We also show that, under the Learning with Errors assumption, our techniques imply that any indistinguishability obfuscator can be converted into one where the size of obfuscated circuits is twice that of the original circuit plus an additive overhead that is polynomial in its depth, input length, and the security parameter. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) represents a growing aspect of how entities, including humans and organizations, are likely to connect with others in their public and private interactions. The exponential rise in the number of IoT devices, resulting from ever-growing IoT applications, also gives rise to new opportunities for exploiting potential security vulnerabilities. In contrast to conventional cryptosystems, frameworks that incorporate fine-grained access control offer better opportunities for protecting valuable assets, especially when the connectivity level is dense. Functional encryption is an exciting new paradigm of public-key encryption that supports fine-grained access control, generalizing a range of existing fine-grained access control mechanisms. This survey reviews the recent applications of functional encryption and the major cryptographic primitives that it covers, identifying areas where the adoption of these primitives has had the greatest impact. We first provide an overview of different application areas where these access control schemes have been applied. Then, an in-depth survey of how the schemes are used in a multitude of applications related to IoT is given, rendering a potential vision of security and integrity that this growing field promises. Towards the end, we identify some research trends and state the open challenges that current developments face for a secure IoT realization. [ABSTRACT FROM AUTHOR]

Big data drive multidimensional convergence and profound innovations among industries and provide novel ways of exploring the world. As they significantly create economic and social value, big data meaningfully impact the implementation and management of information security and privacy protection. Cryptographic technologies are used to protect the security and entire life cycle of big data. The demand for this technology is multiplied when the data are stored in the cloud. They are stored in the cloud in the form of ciphertext, and the driving requirement for data retrieval, sharing, and manipulation places the security of data at risk. The all-or-nothing approach of traditional cryptography systems cannot realize the release and processing of data information with flexible and increasingly fine granularity. Consequently, dealing with the relationship between privacy protection and data utilization, as well as navigating the blurry boundaries between subverting either plaintext or ciphertext, has become a research focus of the current cryptographic trend for protecting big data security. Presently, there are many studies designed to solve these limitations. First, security requirements and source encryption mode for future big data systems and applications are elaborated. Then, focusing on the practical security and functionality of the big data life cycle, including storage, retrieval, sharing, calculation, statistical analysis, and utilization, the research being conducted based on those functions is reviewed. For each cryptographic technology that meets the requirement of each type of big data security or application, security and efficiency comments and sufficient comparison analyses of cryptography schemes or protocols are provided; moreover, the current general problems and development trends are expounded. Because the current innovative research on cryptographic technology was primarily based on the development or improvement of a single solution, the study on the security of the entire big data life cycle from a holistic perspective is extremely limited. Finally, based on surveys and integration of cryptographic techniques, a compatible and comprehensive reference cryptographic architecture for big data security (Z-CABDS) is proposed, which can be used to guide each sub-direction to cooperate with each other to achieve the full life cycle security of big data. Moreover, certain challenges, open problems, and thoughts on future research related to the cryptography of big data security from the viewpoint of the entire big data life cycle are addressed, including views on information theory, the intersection and fusion of technologies, and new technology derivation, which aims to provide a good reference and inspiration for follow-up research. [ABSTRACT FROM AUTHOR]

In multi-client functional encryption (MC-FE) for predicate queries, clients generate ciphertexts of plaintexts $x_{1}, \ldots, x_{n}$ binding with a time period $T$ and store them on a cloud server, and the cloud server receives a function key corresponding to a predicate $f$ from a trusted center and learns whether $f(x_{1}, \ldots, x_{n}) = 1$ or not by running the decryption algorithm on the multiple ciphertexts of the same time period. MC-FE for predicates can be used for a network event or medical data monitoring system based on time series data gathered by multiple clients. In this paper, we propose efficient MC-FE schemes that support conjunctive equality or range queries on encrypted data in the multi-client settings. First, we propose an efficient multi-client hidden vector encryption (MC-HVE) scheme in bilinear groups and prove the selective security with static corruptions. Our MC-HVE scheme is very efficient since a function key is composed of four group elements, a ciphertext consists of $O(\ell)$ group elements where $\ell $ is the size of a plaintext, and the decryption algorithm only requires four pairing operations. Second, we propose an efficient multi-client range query encryption (MC-RQE) scheme and prove the selective weak security with static corruptions. Our MC-RQE scheme which uses a binary tree is efficient since a ciphertext consists of $O(\log D)$ group elements and a function key consists of $O(n \log D)$ group elements where $D$ is the maximum value of a range.

In cloud computing, the current challenge lies in managing massive data, which is a computationally overburdened environment for data users. Outsourced computation can effectively ease the memory and computation pressure on overburdened data storage. We propose an outsourced unbounded decryption scheme in the standard assumption and standard model for large data settings based on inner product computation. Security analysis shows that it can achieve adaptive security. The scheme involves the data owner transmitting encrypted data to a third-party cloud server, which is responsible for computing a significant amount of data. Then the ripe data is handed over to the data user for decryption computation. In addition, there is no need to give the prior bounds of the length of the plaintext vector in advance. This allows for the encryption algorithm to run without determining the length of the input data before the setup phase, that is, our scheme is on the unbounded setting. Through theoretical analysis, the storage overhead and communication cost of the data users remain independent of the ciphertext size. The experimental results indicate that the efficiency and performance are greatly enhanced, about 0.03S for data users at the expense of increased computing time on the cloud server. [ABSTRACT FROM AUTHOR]