Homomorphic Hash Function Research Articles

New tag-based signatures and their applications on linearly homomorphic signatures

Tag-based signature schemes can be efficiently converted into digital signature schemes using a generic transformation. However, there is no signature scheme that admits k>1 fold tag-collisions in the lattice environment as pointed by Ducas and Micciancio (2014). This work answers this problem in the stateful case. We use homomorphic hash functions and hash functions of tags to construct a SIS-based stateful tag-based signature (STS) scheme that admits k>1 fold tag-collisions. Messages are encoded prior to the signing procedure such that any k sequentially signed messages with the same tag form a basis for a vector subspace. The security analysis adopts a new abstraction called vector-space oriented partition. With the same technique, two STS schemes based on the CDH and the RSA assumptions, respectively, are proposed.As an application of our STS schemes, we show that those having field (or quasi-field) as message space can be converted into linearly homomorphic signature (LHS) schemes. Therefore, we immediately obtain CDH/RSA-based LHS scheme in the standard model under the same weaker assumption. Our LHS schemes can be viewed as “removing the restriction on the homomorphic property” from the related STS schemes. They have similar public key and signature sizes as the existing counterparts.

Algebraic Signature-Based Public Data Integrity Batch Verification for Cloud-IoT

With the rapid development of Internet of Things, the related data are growing explosively. However, IoT devices have limited storage and computing capabilities so that they cannot deal with massive data storage and computing locally. The integration of IoT and cloud is regarded as an effective solution to the above issue, i.e., IoT devices outsource collected data to cloud to enjoy powerful storage and computing resources. Because the data stored in cloud are out of the control of IoT users, some security risks need to be addressed in advance. In this paper, we propose a public data integrity verification scheme, called AIVCI, to check the data integrity for Cloud-IoT scenarios. Firstly, based on algebraic signature and homomorphic hash function, AIVCI can efficiently complete data auditing. Secondly, AIVCI adopts blind technology to prevent the privacy leakage of IoT data and further protect the privacy of IoT users. Thirdly, batch auditing is implemented to improve auditing efficiency and meet realistic demands for Cloud-IoT scenarios. And a new data structure named Improved Divide and Conquer Table (ID&CT) is designed to realize efficient data dynamics. Finally, the security and performance analysis demonstrates that AIVCI is more secure and efficient.

Secure and Efficient Smart Healthcare System Based on Federated Learning

The rapid development of smart healthcare system in the Internet of Things (IoT) has made the early detection of many chronic diseases more convenient, quick, and economical. However, when healthcare organizations collect users’ health data through deployed IoT devices, there are issues of compromising users’ privacy. In view of this situation, this paper introduces federated learning technology to solve the problem of data security. In this paper, we consider the two main problems of federated learning applications in IoT smart healthcare system: (1) how to reduce the time overhead of system running and (2) how to authenticate that the user device uploading data is deployed by the system itself. To solve the above problems, we propose the first federated learning scheme based on full dynamic secret sharing. First, we use a two-mask protocol to keep the user’s local model parameters confidential during federated learning. Then, based on homogeneous linear recursive equation, homomorphic hash function, and elliptic curve cryptosystem, the full dynamic secret sharing and user identity authentication are realized. In addition, our scheme allows users to join or quit during training. Finally, we have carried out simulation test on this scheme. The experimental results show that the efficiency of our scheme is improved by about 60% on average in the case of no user dropping and by about 30% in the case of some users dropping.

Conditional Anonymous Certificateless Public Auditing Scheme Supporting Data Dynamics for Cloud Storage Systems

Cloud computing provides users with convenient data storage services, which simultaneously poses various security concerns, the integrity of outsourced data has been termed as one of the most concerning security issues. Certificateless public auditing not only enables a third-party auditor (TPA) to check data integrity, but also avoids complex certificates management and inherent drawbacks of key escrow. Up to date, a few certificateless public auditing schemes have been proposed, without providing fast data dynamics or protecting the identity privacy of users. In this paper, we propose a lightweight conditional anonymous certificateless public auditing (CACPA) scheme, supporting much faster data dynamics in cloud storage systems. Based on a homomorphic hash function, we design a certificateless signature and integrate it into the construction of CACPA, reducing the computational costs of TPA substantially. CACPA achieves conditional identity privacy preservation, anyone cannot infer the real identity of a user based on outsourced data, only the private key generator (PKG) can revoke the users when some misbehaviors occur. We provide security analysis of CACPA, and conduct performance evaluation demonstrating the lightweight advantages of CACPA, and therefore it is suitable for auditors with resource-constrained mobile devices.

An Anonymous Authentication and Key Update Mechanism for IoT Devices Based on EnOcean Protocol.

EnOcean, a commonly used control protocol in smart lighting systems, provides authentication, as well as message integrity verification services, and can resist replay attack and tamper attack. However, since the device identity information transmitted between sensors in smart lighting control systems is easily accessible by malicious attackers, attackers can analyze users’ habits based on the intercepted information. This paper analyzed the security of the EnOcean protocol using a formal analysis method based on the colored Petri net (CPN) theory and the Dolev–Yao attacker model and found that the protocol did not anonymize the device identity information and did not have a communication key update mechanism, so an attacker could easily initiate a key compromise impersonation attack (KCIA) after breaking the pre-shared communication key. To address the above security issues, this paper proposed an EnOcean-A protocol with higher security based on the EnOcean protocol. The EnOcean-A protocol introduced a trusted third-party server to send communication keys to communication devices because devices must obtain different communication keys from the trusted third-party server each time they communicated. Thus, this protocol could resist a KCIA and achieve forward security. Meanwhile, the device identity information was anonymized using a homomorphic hash function in the EnOcean-A protocol, and the dynamic update mechanism of the device identity information was added so that an attacker could not obtain the real identity information of the device. Finally, the formal analysis of the EnOcean-A protocol showed that the new protocol could resist a KCIA and ensure the anonymity and untraceability of the communication device, which had higher security compared with the EnOcean protocol.

Public Integrity Auditing of Shared Encrypted Data within Cloud Storage Group

The pandemic of COVID-19 has posed a severe challenge to the traditional on-site centralized development projects; people therefore have to share data in a group by the cloud storage server and develop projects at home. The cloud server is untrustworthy, although it supplies the powerful computing capability and abundant storage space; so far wide research has been proposed to verify data integrity. Therefore, how to leverage the cloud server and ensure the integrity of the data (especially the encrypted data) stored on the remote cloud devices remains an issue for the clients. To address this issue, we utilize the technique of homomorphic hash function to implement reencryption ciphertext blocks and introduce a certificateless signature scheme for the integrity verification of encrypted data shared within a group. A detailed challenge-and-response game represents that the proposed scheme can preserve encrypted data blocks integrity against the internal/external attacker and malicious cloud service servers. We give the theoretical and experimental performance analysis of the scheme and exhibit that the scheme is efficient and practical.

A data integrity detection method for accounting informatization based on homomorphic hash function

A data integrity detection method for accounting informatization based on homomorphic hash function

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.

Certificateless Multisignature Scheme Suitable for Network Coding

Network coding can save the wireless network resources and improve the network throughput by combining the routing with coding. Traditional multisignature from certificateless cryptosystem is not suitable for the network coding environment. In this paper, we propose a certificateless multisignature scheme suitable for network coding (NC-CLMSS) by using the sequential multisignature and homomorphic hash function. NC-CLMSS is based on the CDH and ECDL problems, and its security is detailedly proved in the random oracle (RO) model. In NC-CLMSS, the source node generates a multisignature for the message, and the intermediate node linearly combines the receiving message. NC-CLMSS can resist the pollution and forgery attacks, and it has the fixed signature length and relatively high computation efficiency.

A verifiable quantum key agreement protocol based on six-qubit cluster states

Quantum key agreement requires all participants to recover the shared key together, so it is crucial to resist the participant attack. In this paper, we propose a verifiable multi-party quantum key agreement protocol based on the six-qubit cluster states. A verifiable distributor who preserves some subsequences of the six-qubit cluster states is introduced into this protocol, thus the participants can not obtain the shared key in advance. Besides, the correctness and simultaneity of the shared key are guaranteed by the trusted design combiner and homomorphic hash function. Furthermore, the security analysis shows that the new protocol can resist the external and internal attacks.

Practical and Provably Secure Distributed Aggregation: Verifiable Additive Homomorphic Secret Sharing

Often clients (e.g., sensors, organizations) need to outsource joint computations that are based on some joint inputs to external untrusted servers. These computations often rely on the aggregation of data collected from multiple clients, while the clients want to guarantee that the results are correct and, thus, an output that can be publicly verified is required. However, important security and privacy challenges are raised, since clients may hold sensitive information. In this paper, we propose an approach, called verifiable additive homomorphic secret sharing (VAHSS), to achieve practical and provably secure aggregation of data, while allowing for the clients to protect their secret data and providing public verifiability i.e., everyone should be able to verify the correctness of the computed result. We propose three VAHSS constructions by combining an additive homomorphic secret sharing (HSS) scheme, for computing the sum of the clients’ secret inputs, and three different methods for achieving public verifiability, namely: (i) homomorphic collision-resistant hash functions; (ii) linear homomorphic signatures; as well as (iii) a threshold RSA signature scheme. In all three constructions, we provide a detailed correctness, security, and verifiability analysis and detailed experimental evaluations. Our results demonstrate the efficiency of our proposed constructions, especially from the client side.

Fine-grained assured data deletion scheme based on attribute association

ABSTRACT With the rapid development of cloud computing technology, an increasing number of enterprises and users store their data in the cloud to achieve convenient storage and data sharing. However, once data is stored in a third-party cloud storage service provider, the privacy and integrity of the data can be compromised. This paper proposes a fine-grained assured data deletion scheme based on attribute association (ADAA), which aims to protect security and achieve assured deletion of outsourced data. The scheme uses standard encryption technology to ensure the privacy and integrity of the data and implements secure deletion based on attribute revocation. At the same time, it incorporates access policy graphs and policy combinations to achieve fine-grained access control and data sharing. In addition, it associates attributes to achieve access control by using a homomorphic hash function, and it establishes a deletion confirmation feedback mechanism to provide a reliable guarantee. By experimenting and comparing with related schemes, ADAA is proved to be safe and efficient, and it provides a practical method for the assured deletion of cloud storage data.

Cloud data integrity verification scheme for associated tags

In typical cloud storage systems such as storage systems for electronic health records (EHRs), users must regularly update sensitive files that are stored in the cloud. Leakage of the operation logs (OLs) will cause severe damage to users. Therefore, it is critical to develop secure auditing techniques for verifying users OLs. Under this scenario, we propose a novel auditing scheme that realizes secure detection for users OLs. In this design, the OLs are grouped according to the types of operations, the verification tags (VTs) of the OLs are calculated by associating an operation relationship, and the cloud data integrity verification is realized by randomly detecting the correctness of VTs. We introduce a homomorphic hash function into the integrity verification of the OLs, and the verification result for the sensitive information is derived from the verification result for the public information, thereby realizing stronger privacy protection. Furthermore, our scheme also achieves efficient handling of data dynamics. The security analysis and experimental verification indicate that our scheme is secure and efficient.

A Provably Secure Distant Data Integrity Checking With Homomorphic Hash Function in Secure Cloud Storage

The cloud storage with user facilities like great data storage quality, higher computing, scalable and flexible has been one of the major application of cloud computing. Large number of data holders is subcontracting the files to the cloud. The cloud server being a public domain is not very reliable thus the data holders are required to find a reliable and trustworthy way to check the ownership of the data files that they subcontract on the cloud server which is present in a remote location. To tackle this drawback number of Distant Data Integrity Checking (DDIC) protocols is in the literature but these protocols have liabilities with respect to the data dynamics and the efficiency. This work recommends a novel DDIC protocol built across homomorphic-hash-function. This system gives a resistance against number of attacks like replay, replaces and falsifications. This work introduces the Operation-record-table(ORT) which is an optimized table resulting in keeping the constant cost for supporting the data variations and keep tracks of blocking the file operations.

Remote Data Integrity Checking in Cloud Storage

As an important application in cloud computing, cloud storage provides data users flexible, scalable and high-quality data computation and storage services. A large number of data users opt to outsource their data files to the cloud storage. Because cloud storage services are not completely reliable, data users require trusty means to check the possession for their data files deployed to remote cloud servers. To overcome this critical issue, a few remote data integrity checking protocols have been presented. But many existing schemes have drawbacks in data dynamics or efficiency. So a new efficient remote data integrity checking protocol based on homomorphic hash function has been developed. The new scheme is certainly safe against replacing attack, replay attack and forgery attack based on a typical security model. To brace data dynamics like inertion, updation or deletion operations on file blocks, an operation record table is included to trace functions on data blocks, and also makes the cost of accessing operation record table almost less or firm. Prototype implementation shows that the scheme is beneficial for realworld applications.

Towards a rational bidding process of initial public offerings: a proposed model for secure auction based on homomorphic computation and secure hash functions

Towards a rational bidding process of initial public offerings: a proposed model for secure auction based on homomorphic computation and secure hash functions

A Novel Efficient Remote Data Possession Checking Protocol in Cloud Storage

As an important application in cloud computing, cloud storage offers user scalable, flexible, and high-quality data storage and computation services. A growing number of data owners choose to outsource data files to the cloud. Because cloud storage servers are not fully trustworthy, data owners need dependable means to check the possession for their files outsourced to remote cloud servers. To address this crucial problem, some remote data possession checking (RDPC) protocols have been presented. But many existing schemes have vulnerabilities in efficiency or data dynamics. In this paper, we provide a new efficient RDPC protocol based on homomorphic hash function. The new scheme is provably secure against forgery attack, replace attack, and replay attack based on a typical security model. To support data dynamics, an operation record table (ORT) is introduced to track operations on file blocks. We further give a new optimized implementation for the ORT, which makes the cost of accessing ORT nearly constant. Moreover, we make the comprehensive performance analysis, which shows that our scheme has advantages in computation and communication costs. Prototype implementation and experiments exhibit that the scheme is feasible for real applications.

Elliptic Curve Multiset Hash

A homomorphic, or incremental, multiset hash function, associates a hash value to arbitrary collections of objects (with possible repetitions) in such a way that the hash of the union of two collections is easy to compute from the hashes of the two collections themselves: it is simply their sum under a suitable group operation. In particular, hash values of large collections can be computed incrementally and/or in parallel. Homomorphic hashing is thus a very useful primitive with applications ranging from database integrity verification to streaming set/multiset comparison and network coding. Unfortunately, constructions of homomorphic hash functions in the literature are hampered by two main drawbacks: they tend to be much longer than usual hash functions at the same security level (e.g. to achieve a collision resistance of 2^128, they are several thousand bits long, as opposed to 256 bits for usual hash functions), and they are also quite slow. In this paper, we introduce the Elliptic Curve Multiset Hash (ECMH), which combines a usual bit string-valued hash function like BLAKE2 with an efficient encoding into binary elliptic curves to overcome both difficulties. On the one hand, the size of ECMH digests is essentially optimal: 2m-bit hash values provide O(2^m) collision resistance. On the other hand, we demonstrate a highly-efficient software implementation of ECMH, which our thorough empirical evaluation shows to be capable of processing over 3 million set elements per second on a 4 GHz Intel Haswell machine at the 128-bit security level---many times faster than previous practical methods.

$${\text {SL}}_2$$ SL 2 homomorphic hash functions: worst case to average case reduction and short collision search

We study homomorphic hash functions into SL(2,q), the 2x2 matrices with determinant 1 over the field with $q$ elements. Modulo a well supported number theoretic hypothesis, which holds in particular for concrete homomorphisms proposed thus far, we provide a worst case to average case reduction for these hash functions: upto a logarithmic factor, a random homomorphism is as secure as _any_ concrete homomorphism. For a family of homomorphisms containing several concrete proposals in the literature, we prove that collisions of length O(log(q)) can be found in running time O(sqrt(q)). For general homomorphisms we offer an algorithm that, heuristically and according to experiments, in running time O(sqrt(q)) finds collisions of length O(log(q)) for q even, and length O(log^2(q)/loglog(q))$ for arbitrary q. While exponetial time, our algorithms are faster in practice than all earlier generic algorithms, and produce much shorter collisions.

Privacy-preserving public auditing for educational multimedia data in cloud computing

Nowadays, as distance learning is being widly used, multimedia data becomes an effective way for delivering educational contents in online educational systems. To handle the educational multimedia data efficiently, many distance learning systems adopt a cloud storage service. Cloud computing and storage services provide a secure and reliable access to the outsourced educational multimedia contents for users. However, it brings challenging security issues in terms of data confidentiality and integrity. The straightforward way for the integrity check is to make the user download the entire data for verifying them. But, it is inefficient due to the large size of educational multimedia data in the cloud. Recently many integrity auditing protocols have been proposed, but most of them do not consider the data privacy for the cloud service provider. Additionally, the previous schemes suffer from dynamic management of outsourced data. In this paper, we propose a public auditing protocol for educational multimedia data outsourced in the cloud storage. By using random values and a homomorphic hash function, our proposed protocol ensures data privacy for the cloud and the third party auditor (TPA). Also, it is secure against lose attack and temper attack. Moreover, our protocol is able to support fully dynamic auditing. Security and performance analysis results show that the proposed scheme is secure while guaranteeing minimum extra computation costs.