Efficient Secure Outsourcing Computation of Matrix Multiplication in Cloud Computing

Zhang, Shenmin; Li, Hongwei; Jia, Kun; Zhao, Lian

doi:10.1109/glocom.2016.7841783

Cited by 20 publications

(19 citation statements)

References 14 publications

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“…There are also several works such as [36][37][38][39] addressing the result integrity of matrix multiplication in public cloud using cryptographic methods. Since they do not assume any restricting rules on the cloud side, they can be applied on the MapReduce model as well.…”

Section: Related Workmentioning

confidence: 99%

Efficient verification of parallel matrix multiplication in public cloud: the MapReduce case

2020

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With the advent of cloud-based parallel processing techniques, services such as MapReduce have been considered by many businesses and researchers for different applications of big data computation including matrix multiplication, which has drawn much attention in recent years. However, securing the computation result integrity in such systems is an important challenge, since public clouds can be vulnerable against the misbehavior of their owners (especially for economic purposes) and external attackers. In this paper, we propose an efficient approach using Merkle tree structure to verify the computation results of matrix multiplication in MapReduce systems while enduring an acceptable overhead, which makes it suitable in terms of scalability. Using the Merkle tree structure, we record fine-grained computation results in the tree nodes to make strong commitments for workers; they submit a commitment value to the verifier which is then used to challenge their computation results’ integrity using elected input data as verification samples. Evaluation outcomes show significant improvements comparing with the state-of-the-art technique; in case of 300*300 matrices, 73% reduction in generated proof size, 61% reduction in the proof construction time, and 95% reduction in the verification time.

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Section: Related Workmentioning

confidence: 99%

Efficient verification of parallel matrix multiplication in public cloud: the MapReduce case

2020

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“…Subsequent works for verifiable computation of the general function include other works, 6,12,22,23 and the previous works [24][25][26][27][28] concentrate on schemes to improve the efficiency for computing the specific function. Subsequent works for verifiable computation of the general function include other works, 6,12,22,23 and the previous works [24][25][26][27][28] concentrate on schemes to improve the efficiency for computing the specific function.…”

Section: Related Workmentioning

confidence: 99%

“…Gennaro et al 2 first introduced and formalized the non-interactive verifiable outsourced computation. Subsequent works for verifiable computation of the general function include other works, 6,12,22,23 and the previous works [24][25][26][27][28] concentrate on schemes to improve the efficiency for computing the specific function. Several previous schemes only allow one specific client to privately verify the result during each round of computational process, which has some inherent limitations as introduced above.…”

Section: Related Workmentioning

confidence: 99%

An improved scheme for outsourced computation with attribute‐based encryption

Yang

Sun

Qin

et al. 2018

Concurrency and Computation

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With the wide deployment of cloud computing, outsourcing complicated computational tasks to cloud service providers has attracted much attention. An increasing number of clients with computationally constrained devices choose to outsource their heavy tasks to cloud servers to reduce the computational overhead in local. However, how to preserve the integrity of computational results becomes a challenge since commercial cloud servers are not trusted. Public verifiability is an effective mechanism to allow clients to verify the integrity of the results returned by the servers. Because the results are sensitive in many applications, it raises the problem of privacy leakage in the public verification process. In this paper, we propose an efficient verifiable computation scheme while keeping output privacy. The proposed scheme achieves blind verifiability such that the verifiers who have not the additional information (retrieve key) can verify the integrity of the result without learning the result. Furthermore, by combining with (k, n)-threshold sharing, our scheme allows the clients jointly learn the results. KEYWORDSblind verifiability, cloud computing, KP-ABE, verifiable computation INTRODUCTIONWith the rapid development of cloud technology, outsourced computation has been an important field. The technology provides not only the storage service but also the computing service, which can benefit the clients with limited resources a great deal. Meanwhile, the computationally constrained devices such as smartphones, laptops, and wearable equipments 1 have become popular. These scenarios contribute to the research of outsourced computation where the computationally weak clients (devices) outsource the expensive and complicated computation to the large and powerful servers. 2 In addition, outsourced computation is sometimes used to perform the complicated task in some cryptographic primitives 3-5 as well, which can make the primitives more efficient and practical. However, the servers are generally operated by the third-party outside the control domain of clients, and there may exist an incentive for servers to deceive clients into accepting an incorrect result. For instance, to save the computational resources, the cloud servers may not perform the advertised computation but instead return a computationally indistinguishable result to the client.In this case, the key problem is that the clients may not have enough computational resources to detect the incorrect result, which would bring trouble to clients in their subsequent works. For example, if the computational result is the statistics analysis for stock market, the incorrect result will cause the investment corporation property loss. Hence, clients need some efficient methodologies to guarantee that the returned result from servers is correct, which is a necessity in the outsourced computation paradigm.The design of verifiable outsourced computation has drawn widespread attentions. 2,6-9 In the verifiable computation scheme, the verifiability property allows the cl...

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“…They compared their scheme functionalities with existing works, such as Fiore's [7], Li's [16], and Jia's schema [11]. Zhang et al [23] proved that their schema is more efficient in terms of functionality as well as computation, storage, and communication overhead.…”

Section: Related Workmentioning

confidence: 99%

“…Zhang et al [23] presented an outsourcing computation schema in an amortized model for matrix multiplication of two arbitrary matrices that meet the requirements for both security and efficiency. They compared their scheme functionalities with existing works, such as Fiore's [7], Li's [16], and Jia's schema [11].…”

Section: Related Workmentioning

confidence: 99%

Parallel Hill Cipher Encryption Algorithm

Haj¹,

Qatawneh²

2018

IJCA

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Cryptography is the discipline of encoding and decoding messages. Cryptography is used frequently in people's daily lives to keep sensitive information, such as credit card information, safe. Many everyday activities can be easily monitored by unintended third parties via Internet. Hill cipher is a classic cryptography based on linear algebra that is simply a linear transformation represented by a matrix. The encoding and decoding process in Hill cipher involves matrix multiplication, which is potentially time consuming, making it one of the most well-studied problems in this field. In this paper, we implement the message passing interface (MPI) and MapReduce methods to demonstrate their effectiveness in expediting Hill cipher algorithm in parallel algorithms on a multi-core system. Simulation results show that the efficiency rates of MPI and MapReduce are 93.71 % and 53.43 respectively, with a multi-core processor on the large file size, indicating better performances compared with sequential methods.

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Efficient Secure Outsourcing Computation of Matrix Multiplication in Cloud Computing

Cited by 20 publications

References 14 publications

Efficient verification of parallel matrix multiplication in public cloud: the MapReduce case

Efficient verification of parallel matrix multiplication in public cloud: the MapReduce case

An improved scheme for outsourced computation with attribute‐based encryption

Parallel Hill Cipher Encryption Algorithm

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