Bilinear Maps Research Articles

Efficient Zero-Knowledge Proofs of Graph Signature for Connectivity and Isolation Using Bilinear-Map Accumulator

To prove the graph relations such as the connectivity and the isolation for a certified graph, the system of graph signature and proofs have been proposed. In this system, an issuer generates a signature certifying the topology of an undirected graph, and issues a prover the signature. The prover can prove the knowledge of the signature and the graph in the zero-knowledge, i.e., the signature and the signed graph are hidden. In addition, the prover can prove relations on the certified graph such as the connectivity and isolation between two vertexes. In the previous system, using integer commitments on RSA modulus, the graph relations are proved. However, the RSA modulus needs a longer size of each element. Furthermore, the proof size and the verification cost depend on the total numbers of vertexes and edges. In this paper, we propose a graph signature and proof system, where these are computed on bilinear groups without the RSA modulus. Moreover, using a bilinear map accumulator, the prover can prove the connectivity and isolation on a graph, where the proof size and verification cost become independent from the total numbers of vertexes and edges.

A Secure Pseudonym-Based Conditional Privacy-Preservation Authentication Scheme in Vehicular Ad Hoc Networks.

Existing identity-based schemes utilized in Vehicular Ad hoc Networks (VANETs) rely on roadside units to offer conditional privacy-preservation authentication and are vulnerable to insider attacks. Achieving rapid message signing and verification for authentication is challenging due to complex operations, such as bilinear pairs. This paper proposes a secure pseudonym-based conditional privacy-persevering authentication scheme for communication security in VANETs. The Elliptic Curve Cryptography (ECC) and secure hash cryptographic function were used in the proposed scheme for signing and verifying messages. After a vehicle receives a significant amount of pseudo-IDs and the corresponding signature key from the Trusted Authority (TA), it uses them to sign a message during the broadcasting process. Thus, the proposed scheme requires each vehicle to check all the broadcasting messages received. Besides, in the proposed scheme, the TA can revoke misbehaving vehicles from continuously broadcasting signed messages, thus preventing insider attacks. The security analysis proved that the proposed scheme fulfilled the security requirements, including identity privacy-preservation, message integrity and authenticity, unlinkability, and traceability. The proposed scheme also withstood common security attacks such as man-in-the-middle, impersonation, modification, and replay attacks. Besides, our scheme was resistant against an adaptive chosen-message attack under the random oracle model. Furthermore, our scheme did not employ bilinear pairing operations; therefore, the performance analysis and comparison showed a lower resulting overhead than other identity-based schemes. The computation costs of the message signing, individual signature authentication, and batch signature authentication were reduced by 49%, 33.3%, and 90.2%, respectively.

A traceable privacy‐aware data publishing platform on permissioned blockchain

AbstractThe data publishing platform based on blockchain has advantages of decentralization, anti‐tampering function, and distributed storage. However, privacy leakage, untraceability, and the data tampering cannot be addressed. In order to solve these problems, this article proposes an aggregation signature scheme based on the bilinear mapping, and proposes a traceable privacy‐aware data publishing platform by applying the aggregation signature scheme to the data publishing platform on permissioned blockchain. Performance analysis shows that the traceable privacy‐aware data publishing platform reduces the communication cost, improves the efficiency of signature verification and transmission, provides the anonymity and traceability for the users in the platform.

Exponential Ant-Lion Rider Optimization for Privacy Preservation in Cloud Computing

The innovative trend of cloud computing is outsourcing data to the cloud servers by individuals or enterprises. Recently, various techniques are devised for facilitating privacy protection on untrusted cloud platforms. However, the classical privacy-preserving techniques failed to prevent leakage and cause huge information loss. This paper devises a novel methodology, namely the Exponential-Ant-lion Rider optimization algorithm based bilinear map coefficient Generation (Exponential-AROA based BMCG) method for privacy preservation in cloud infrastructure. The proposed Exponential-AROA is devised by integrating Exponential weighted moving average (EWMA), Ant Lion optimizer (ALO), and Rider optimization algorithm (ROA). The input data is fed to the privacy preservation process wherein the data matrix, and bilinear map coefficient Generation (BMCG) coefficient are multiplied through Hilbert space-based tensor product. Here, the bilinear map coefficient is obtained by multiplying the original data matrix and with modified elliptical curve cryptography (MECC) encryption to maintain data security. The bilinear map coefficient is used to handle both the utility and the sensitive information. Hence, an optimization-driven algorithm is utilized to evaluate the optimal bilinear map coefficient. Here, the fitness function is newly devised considering privacy and utility. The proposed Exponential-AROA based BMCG provided superior performance with maximal accuracy of 94.024%, maximal fitness of 1, and minimal Information loss of 5.977%.

A weighted bilinear neural collaborative filtering approach for drug repositioning.

Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug-disease associations. Similar to traditional latent factor models, which directly factorize drug-disease associations, they assume the neighbors are independent of each other in the network and thus tend to be ineffective to capture localized information. In this study, we propose a novel neighborhood and neighborhood interaction-based neural collaborative filtering approach (called DRWBNCF) to infer novel potential drugs for diseases. Specifically, we first construct three networks, including the known drug-disease association network, the drug-drug similarity and disease-disease similarity networks (using the nearest neighbors). To take the advantage of localized information in the three networks, we then design an integration component by proposing a new weighted bilinear graph convolution operation to integrate the information of the known drug-disease association, the drug's and disease's neighborhood and neighborhood interactions into a unified representation. Lastly, we introduce a prediction component, which utilizes the multi-layer perceptron optimized by the α-balanced focal loss function and graph regularization to model the complex drug-disease associations. Benchmarking comparisons on three datasets verified the effectiveness of DRWBNCF for drug repositioning. Importantly, the unknown drug-disease associations predicted by DRWBNCF were validated against clinical trials and three authoritative databases and we listed several new DRWBNCF-predicted potential drugs for breast cancer (e.g. valrubicin and teniposide) and small cell lung cancer (e.g. valrubicin and cytarabine).

Pairing-free certificateless authenticated encryption with keyword search

Public key authenticated encryption with keyword search (PAEKS) is a cryptographic primitive which ensures privacy of user’s sensitive data while preserving the search ability over them. Of particular interest are certificateless PAEKS (CLAEKS) schemes which can further circumvent challenges such as certificate management and key escrow problem. Our goal in this paper is advancing existing literature on certificateless PAEKS schemes in two aspects. First, we notice that published security model of CLAEKS schemes assumes the existence of a single key generation center (KGC) to manage all the keys. Considering multiple KGCs can lead to enhancements in trust-related issues and appears more in line with reality, so we formulate the security model to cover CLAEKS with multiple KGCs. Second, as far as we know, all existing secure CLAEKS schemes are based on the time-consuming bilinear pairing operation which dramatically increases the computational cost. We construct an efficient pairing-free CLAEKS scheme and prove its security in our multiple-KGC security model. Our construction achieves security against keyword guessing attacks performed by both inside and outside adversaries. A comparison between our proposed scheme and other related schemes is also provided to assert the overall superiority of the proposed scheme.

An Efficient and Secure Revocation-Enabled Attribute-Based Access Control for eHealth in Smart Society.

The ever-growing ecosystem of the Internet of Things (IoT) integrating with the ever-evolving wireless communication technology paves the way for adopting new applications in a smart society. The core concept of smart society emphasizes utilizing information and communication technology (ICT) infrastructure to improve every aspect of life. Among the variety of smart services, eHealth is at the forefront of these promises. eHealth is rapidly gaining popularity to overcome the insufficient healthcare services and provide patient-centric treatment for the rising aging population with chronic diseases. Keeping in view the sensitivity of medical data, this interfacing between healthcare and technology has raised many security concerns. Among the many contemporary solutions, attribute-based encryption (ABE) is the dominant technology because of its inherent support for one-to-many transfer and fine-grained access control mechanisms to confidential medical data. ABE uses costly bilinear pairing operations, which are too heavy for eHealth’s tiny wireless body area network (WBAN) devices despite its proper functionality. We present an efficient and secure ABE architecture with outsourcing intense encryption and decryption operations in this work. For practical realization, our scheme uses elliptic curve scalar point multiplication as the underlying technology of ABE instead of costly pairing operations. In addition, it provides support for attribute/users revocation and verifiability of outsourced medical data. Using the selective-set security model, the proposed scheme is secure under the elliptic curve decisional Diffie–Hellman (ECDDH) assumption. The performance assessment and top-ranked value via the help of fuzzy logic’s evaluation based on distance from average solution (EDAS) method show that the proposed scheme is efficient and suitable for access control in eHealth smart societies.

Security Analysis of a Privacy-Preserving Identity-Based Encryption Architecture

Identity-Based Encryption (IBE) has seen limited adoption, largely due to the absolute trust that must be placed in the private key generator (PKG)—an authority that computes the private keys for all the users in the environment. Several constructions have been proposed to reduce the trust required in the PKG (and thus preserve the privacy of users), but these have generally relied on unrealistic assumptions regarding non-collusion between various entities in the system. Unfortunately, these constructions have not significantly improved IBE adoption rates in real-world environments. In this paper, we present a construction that reduces trust in the PKG without unrealistic non-collusion assumptions. We achieve this by incorporating a novel combination of digital credential technology and bilinear maps, and making use of multiple randomly-chosen entities to complete certain tasks. The main result and primary contribution of this paper are a thorough security analysis of this proposed construction, examining the various entity types, attacker models, and collusion opportunities in this environment. We show that this construction can prevent, or at least mitigate, all considered attacks. We conclude that our construction appears to be effective in preserving user privacy and we hope that this construction and its security analysis will encourage greater use of IBE in real-world environments.

Multiple graph fusion based on Riemannian geometry for motor imagery classification

In motor imagery-based brain-computer interfaces (BCIs), the spatial covariance features of electroencephalography (EEG) signals that lie on Riemannian manifolds are used to enhance the classification performance of motor imagery BCIs. However, the problem of subject-specific bandpass frequency selection frequently arises in Riemannian manifold-based methods. In this study, we propose a multiple Riemannian graph fusion (MRGF) model to optimize the subject-specific frequency band for a Riemannian manifold. After constructing multiple Riemannian graphs corresponding to multiple bandpass frequency bands, graph embedding based on bilinear mapping and graph fusion based on mutual information were applied to simultaneously extract the spatial and spectral features of the EEG signals from Riemannian graphs. Furthermore, with a support vector machine (SVM) classifier performed on learned features, we obtained an efficient algorithm, which achieves higher classification performance on various datasets, such as BCI competition IIa and in-house BCI datasets. The proposed methods can also be used in other classification problems with sample data in the form of covariance matrices.

Multimodal Cross-Layer Bilinear Pooling for RGBT Tracking

Hierarchical deep features can provide multilevel abstractions of target objects, which play an important role in target localization and classification. Determining how to effectively aggregate abstract information from different levels in RGB and thermal modalities is the key to exploiting their complementary advantages for robust RGBT tracking. However, existing RGBT tracking algorithms either focus on the semantic information of the last layer or aggregate hierarchical deep features from each modal using simple operations (e.g., summation and concatenation), which limit the capability of the multimodal tracker. To address these issues, in this paper, we propose a novel multimodal cross-layer bilinear pooling network for RGBT tracking. In our network, firstly, to boost the performance of the tracker, we use a channel attention mechanism to implement the adaptive calibration of feature channels for all convolutional layer features before realizing hierarchical feature fusion. Then, a bilinear pooling operation is performed on any two layers through the cross product, which is a second-order computation that effectively aggregates the deep semantic and shallow texture information of the target. Finally, a quality-aware fusion module is designed to aggregate the bilinear pooling features of different layer interactions between different modalities in an adaptive manner. The results of a large number of experiments on two public benchmark datasets demonstrate the effectiveness of our tracker compared with other state-of-the-art tracking methods.

Attribute-Based Blind Signature Scheme Based on Elliptic Curve Cryptography

Blind signature is a special digital signature that allows the signer to sign a document without knowing its content. However, in many situations, multiple people need to blindly sign messages. At this time, the traditional blind signature can no longer satisfy the application requirements. To solve this problem, attribute-based cryptography has been combined with the blind signature. The concept of the attribute-based blind signature is generated. At present, all attribute-based blind signature schemes require the support of bilinear pairing technology, which involves several complex pairing and exponential operations in the signature and verification processes, and the computational efficiency is not high. In this paper, we present an attribute-based blind signature scheme based on elliptic curve cryptography (ECC), and the security of new scheme is proved under the intractability of elliptic curve discrete logarithm problem (ECDLP). Our scheme is a key policy attribute-based signature (KP-ABS). The new scheme uses linear secret sharing scheme (LSSS) matrix technology that does not require recursive operation to achieve more flexible and fine-grained access control. In addition, the scheme is based on Elliptic Curve Cryptography (ECC) using scalar multiplication on an elliptic curve instead of a bilinear pairing operation. Our scheme has significant advantages in terms of computational efficiency and storage compared with existing attribute-based blind signature schemes.

MSR-DoS: Modular Square Root-Based Scheme to Resist Denial of Service (DoS) Attacks in 5G-Enabled Vehicular Networks

Traffic safety and efficiency are extremely significant in both private and public transportation. The fifth-generation (5G)-enabled vehicular networks works wirelessly to share information among vehicles for helping drivers and passengers. Since the vehicle broadcasts the traffic status messages, privacy and security are considered as a challenging issue in 5G-enabled vehicular networks. In order to satisfy these privacy and security requirements, many privacy-preserving and security attacks schemes have been proposed. Nevertheless, since these schemes use a complex elliptic curve and bilinear pair cryptography operations, the performance efficiency of in terms of communication and computational costs is not satisfactory, which denial of service (DoS) attacks occurs. To address this, this paper proposes modular square root-based to resist denial of service (DoS) attacks (MSR-DoS) scheme in 5G-enabled vehicular networks. Our MSR-DoS scheme satisfies authenticity of source, integrity of message, pseudonym privacy-preserving, unlinkable, traceable and revocable in vehicular networks. The security of our work is proved under burrows abadi needham (BAN) logic. The performance analysis and comparison shows that MSR-DoS scheme has less communication and computational costs as compared to the most recent existing works. Meanwhile, the proposed MSR-DoS scheme reduces the computation overhead of signing the message and verifying the message by 99.80% and 98.55%, respectively.

An Improved Action Recognition Network With Temporal Extraction and Feature Enhancement

Image classification and action recognition are both active research topics in the field of computer vision. However, the development of action recognition is rather slow compared with image classification, due to the difficulties in spatial-temporal information modeling. In this paper, we present TEFE, a deep structure combining temporal extraction with feature enhancement to explore the spatial coherence across temporal dimension. The temporal extraction (TE) module is used to capture the short-term and long-term temporal features. Considering the spatial and temporal cues are fine-grained, we believe such cues (if encoded by well-designed bilinear models) could enhance the representation of actions in videos. The feature enhancement (FE) module approximates bilinear pooling operations, which greatly reduce the amount of parameters exist in original bilinear pooling. Extensive experiments have been conducted on mainstream datasets of human action (Something - Something V1 & V2, and Jester). Experimental results show that our model achieves competitive performances than some existing methods.

Efficient Anonymous Authentication for Wireless Body Area Networks

Advances in integrated circuit and wireless communication technologies are making wireless body area networks (WBANs) an increasingly important medical paradigm. By collecting, uploading, and processing real-time physical parameters, WBANs assist clients in better recognizing and managing their bodies. Besides conveniences it brings, WBANs are facing the risk of clients’ privacy leakage during data transmission. Anonymous authentication schemes were proposed to resolve this challenge, and latest schemes ensure that even if a WBAN client’s private key is exposed, previous session keys generated by this client cannot be compromised (known as forward security). Unfortunately, previous forward secure schemes need bilinear pairing operations, which is undesirable in computation-resource-bounded WBANs. Furthermore, the property, that once a WBAN client’s private key is exposed, previous sessions shouldn’t be identified (dubbed forward anonymity), hasn’t been considered in existing works. In response to the above challenges, in this paper, we propose an identity-based authenticated encryption method without pairing, and based on this method, we construct an anonymous authentication scheme. Subsequent security and performance analyses demonstrate that our schemes are secure (including forward anonymous) under the random oracle model, and practical in WBANs with limited resources.

Efficient Pairing-Free Attribute-Based Blind Signature Scheme Based on Ordered Binary Decision Diagram

Attribute-based blind signature is used to realize the blind signature of messages by multiple people, and it is suitable for electronic payment services in the cloud. Most existing attribute-based blind signature schemes have complex bilinear pairing operations in the process of signature and verification, which leads to low algorithm efficiency. To solve this problem, in this study, an efficient pairing-free attribute-based blind signature scheme based on ordered binary decision diagram is proposed, and the security analysis of the new scheme is given under the difficulty of the elliptic curve discrete logarithm problem (ECDLP). The proposed scheme is based on elliptic curve cryptography (ECC). In this scheme, scalar multiplication on the elliptic curve, which is relatively lightweight, is used to replace bilinear pairing with a large computational cost in traditional attribute-based blind signature schemes. The computational overhead in the signature and verification processes is reduced, making the solution more suitable for resource-constrained cloud terminal user scenarios. On the other hand, in order to improve the computational efficiency of the attribute-based blind signature scheme, a more expressive and computationally efficient ordered binary decision diagram (OBDD) structure is used to construct the access structure of the signer. The OBDD access structure can simultaneously support the positive and negative values of attributes in the access structure at the same time. Compared with existing attribute-based blind signature schemes, the proposed scheme has better performance and storage, and meets the needs of resource-constrained cloud end users.

Jordan maps and zero Lie product determined algebras

Let $A$ be an algebra over a field $F$ with $(F)\ne 2$. If $A$ is generated as an algebra by $[[A,A],[A,A]]$, then for every skew-symmetric bilinear map $\Phi:A\times A\to X$, where $X$ is an arbitrary vector space over $F$, the condition that $\Phi(x^2,x)=0 $ for all $x\in A$ implies that $\Phi(xy,z) +\Phi(zx,y) + \Phi(yz,x)=0$ for all $x,y,z\in A$. This is applicable to the question of whether $A$ is zero Lie product determined and is also used in proving that a Jordan homomorphism from $A$ onto a semiprime algebra $B$ is the sum of a homomorphism and an antihomomorphism.

1-Round Distributed Key Generation With Efficient Reconstruction Using Decentralized CP-ABE

Distributed key generation (DKG) is widely used in multi-party computation and decentralized applications. DKG has two phases, namely sharing and reconstruction. Most of the prior DKG protocols need at least 2 rounds for the sharing phase, in case some party raises a dispute. The existing 1-round DKG protocol [Fouque <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , PKC’01], built based on a publicly verifiable secret sharing (PVSS) scheme, assumes a static adversary model and its reconstruction phase requires <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{2})$ </tex-math></inline-formula> communication complexity. Motivated by the observation that a ciphertext-policy attribute-based encryption (CP-ABE) scheme hides secret sharing (SS) in ciphertext, we utilize decentralized CP-ABE to achieve the first adaptively secure 1-round DKG protocol. Firstly, a CP-ABE scheme enables the ciphertexts in DKG to be externally decrypted, making our protocol superior to the PVSS-based DKG protocol in reconstruction. The communication and computation complexities are both lowered to <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> thanks to the constant-sized decryption key and the proposed batch decryption. The use of CP-ABE also makes our DKG protocol storage-friendly, i.e., the parties store no ciphertext after the sharing phase. Secondly, we add non-interactive zero-knowledge (NIZK) proofs to make the CP-ABE ciphertext publicly verifiable by leveraging the sigma protocol and the Fiat-Shamir heuristic. Thirdly, we demonstrate our protocol’s feasibility by presenting a proof-of-concept implementation over Ethereum, which is used as a public channel and a trustworthy computation platform. The implementation is a non-trivial task due to Ethereum’s incompatibility with the bilinear mapping group.

Towards Public Integrity Audition for Cloud-IoT Data Based on Blockchain

With the rapidly developing of Internet of Things (IoT), the volume of data generated by IoT systems is increasing quickly. To release the pressure of data management and storage, more and more enterprises and individuals prefer to integrate cloud service with IoT systems, in which the IoT data can be outsourced to cloud server. Since cloud service provider (CSP) is not fully trusted, a variety of methods have been proposed to deal with the problem of data integrity checking. In traditional data integrity audition schemes, the task of data auditing is usually performed by Third Party Auditor (TPA) which is assumed to be trustful. However, in real-life TPA is not trusted as people thought. Therefore, these schemes suffer from the underlying problem of single-point failure. Moreover, most of the traditional schemes are designed by RSA or bilinear map techniques which consume heavy computation and communication cost. To overcome these shortcomings, we propose a novel data integrity checking scheme for cloud-IoT data based on blockchain technique and homomorphic hash. In our scheme, the tags of all data blocks are computed by a homomorphic hash function and stored in blockchain. Moreover, each step within the process of data integrity checking is signed by the performer, and the signatures are stored in blockchain through smart contracts. As a result, each behavior for data integrity checking in our scheme can be traced and audited which improves the security of the scheme greatly. Furthermore, batch-audition for multiple data challenges is also supported in our scheme. We formalize the system model of our scheme and give the concrete construction. Detailed performance analyses demonstrate that our proposed scheme is efficient and practical without the trust-assumption of TPA.

A Batch Authentication Design to Protect Conditional Privacy in Internet of Vehicles

Internet of vehicles (IoV), a novel technology, holds paramount importance within the transportation domain due to its ability to increase traffic efficiency and safety. Information privacy is of vital importance in IoV when sharing information among vehicles. However, due to the openness of the communication network, information sharing is vulnerable to potential attacks, such as impersonation, modification, side-channel and replay attacks, and so on. In order to resolve the aforementioned problem, we present a conditional privacy-preserving batch authentication (CPPBA) scheme based on elliptic curve cryptography (ECC). The proposed scheme avoids the certificate management problem, conducing to efficiency improvement. When a message is transmitted by a vehicle, its pseudo identity rather than the real identity is also broadcasted along with the shared message, which protects the privacy of the vehicle’s identity. But this privacy is conditional because TA and only the TA can reveal the real identity of the vehicle by tracing. The proposed scheme is batch verifiable, which reduces the computation costs. In addition, our scheme does not involve bilinear pairing operations and does not use the map-to-point hash function, thus making the verification process more effective. An exhaustive efficiency comparison has been carried to show that the proposed CPPBA scheme has lower computation, communication, and storage overheads than the state-of-the-art ones. A relatively comprehensive security analysis has also been carried, which not only shows that the signature design in the CPPBA scheme is unforgeable under the random oracle model but also illustrates that the CPPBA scheme is resistant to various potential attacks. The security is also verified by a popular automated simulation tool, that is, AVISPA.

IPSadas: Identity‐privacy‐aware secure and anonymous data aggregation scheme

Intelligent systems are technologically advanced machines that can sense and respond to the surrounding environment. They have been widely used in medicine, military, transportation, automation, and other fields. However, when these systems deal with their environments, problems such as leakage of identities may occur. The adversary can damage the system communication and attack important nodes. To handle resource-constrained wireless sensor network environments, we propose a secure and anonymous data aggregation scheme. First, based on the bilinear mapping operation and onion routing concepts, we propose a key negotiation and secure information transmission scheme, which conducts confidential transmission and anonymous forwarding of messages in data aggregation. Second, an aggregation routing scheme based on link direction and residual energy is proposed to pledge messages that can arrive the base station without passing through many nodes, which saves network resources to a certain extent. Third, on the basis of the first two contributions, we propose an identity-privacy-aware secure and anonymous data aggregation scheme that protects the identity's privacy. This scheme can conceal the real identity of important nodes and protect the anonymity of messages and link relationships. In addition, an anonymous identity update and synchronization scheme is also proposed to ensure the reliability and security of communication. Meanwhile, our performance evaluations and simulations show that the proposed framework is more effective than several standard schemes with respect to the ability against various attacks, security, and overhead.