Enabling efficient publicly verifiable outsourcing computation for matrix multiplication

Li, Hongwei; Zhang, Shenmin; Luan, Tom H.; Ren, Hao; Zhou, Liang

doi:10.1109/atnac.2015.7366787

Cited by 10 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the protocol, clients jointly generate the key to PRF. We can refer to more PRF-based works for different application domains, such as [67], [68] for linear algebra, [69] for pattern matching, and [70] for data search. Moreover, oblivious PRF has also attracted the attention of researchers.…”

Section: B Pseudorandom Functionsmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

View full text Add to dashboard Cite

With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field. INDEX TERMS Secure outsourced computing, privacy preserving, homomorphic encryption, secure outsourced machine learning, data processing.

show abstract

Section: B Pseudorandom Functionsmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Kumar et al [13] evaluated their algorithm on security, efficiency, and variability parameters. With high efficiency and practical usability, their algorithm can mostly replace costly cryptographic operations and securely solve matrix multiplication algorithm.…”

Section: Related Workmentioning

confidence: 99%

Parallel Hill Cipher Encryption Algorithm

Haj¹,

Qatawneh²

2018

IJCA

View full text Add to dashboard Cite

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.

show abstract