Efficient and Secure Outsourcing Scheme for RSA Decryption in Internet of Things

Zhang, Hanlin; Yu, Jia; Tian, Chengliang; Tong, Le; Lin, Jie; Ge, Linqiang; Wang, Huaqun

doi:10.1109/jiot.2020.2970499

Cited by 20 publications

(13 citation statements)

References 40 publications

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“…The algorithms which only rely on global attributes often take the whole network structure as a parameter until all community structures are detected, which leads to the high complexity and it is not suitable for large-scale networks with complex structures like IoT [20], [21].…”

Section: The Non-overlapping Community Detection Algorithms 1) Thementioning

confidence: 99%

A Novel Community Detection Algorithm Based on Paring, Splitting and Aggregating in Internet of Things

Xia

Zhang

et al. 2020

IEEE Access

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The explosive growth of Internet of Things (i.e., IoT) terminal equipment makes its topology more complex, which leads to the increasing cost of network research. Recently, the implicit community structure is widely used to improve the efficiency of research. However, most of the non-overlapping community detection algorithms have some weakness, such as the large number of community detected and the obvious scale gap between communities. To address these abovementioned problems, we design a novel non-overlapping community detection algorithm, named as Pairing, Splitting and Aggregating algorithm (i.e., PSA). Firstly, in order to improve the accuracy of community division, a new node similarity index is designed to transform the network into a large number of similar node pairs. Secondly, based on the connected branches composed of similar node pairs, the network is further divided into several similar node sets. Thirdly, to balance the scale gap of different communities, the Grasshopper Optimization Algorithm, (i.e., GOA) is introduced to combine the local attribute (i.e., conductance) and global attribute (i.e., modularity) together to aggregate similar node sets into potential (or final) communities. Finally, the experimental results show that PSA not only controls the difference among communities well, but also outperforms the other four popular algorithms in terms of two metrics. Moreover, we propose a community-based resource discovery method (or scheme), named as Community-assisted Short-distancequery Resource Discovery algorithm (i.e., CSRD) to further verify the efficiency of PSA. The results show that the resource discovery efficiency of CSRD using PSA is better compared with other algorithms.

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Section: The Non-overlapping Community Detection Algorithms 1) Thementioning

confidence: 99%

A Novel Community Detection Algorithm Based on Paring, Splitting and Aggregating in Internet of Things

Xia

Zhang

et al. 2020

IEEE Access

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“…We first provide a short description of the classical "textbook" RSA public-key encryption scheme [15] (using the notations from [18]): It has been proposed as soon as in 1982 by Quisquater and Couvreur [12] to use the Chinese Remainder Theorem (CRT) in order to improve the efficiency of the decryption algorithm. The "textbook" RSA-CRT public-key encryption scheme is modified as follows: Key Generation: With the same notation as above, the algorithm additionally computes…”

Section: Description Of Zhang Et Al's Protocolmentioning

confidence: 99%

“…Following Kerckhoff's principles [6], it is natural to assume that the adversary knows the public key (n, e) it attacks. Zhang et al [18], indeed do not add the value of the public exponent e to the list of secret inputs in their "security proof". Actually, in practical applications, RSA users very often use e = 2 16 + 1 = 65537 as the public exponent.…”

Section: A Passive Attack On the Protocol Privacymentioning

confidence: 99%

“…This increases the running time only by a constant factor. Zhang et al's protocol does not provide privacy since this probabilistic polynomial time recovers the plaintext, the (secret) decryption exponent and the factorization of n from a passive eavesdropping of a single execution of the protocol (and Theorem 1 from [18] is therefore flawed). Remark 1.…”

Section: A Passive Attack On the Protocol Privacymentioning

confidence: 99%

“…This problem is particularly challenging for public-key cryptography such as the RSA primitive [15]. In [18], Zhang, Yu, Tian, Tong, Lin, Ge and Wang designed an efficient outsourcing scheme for RSA decryption in IoT. RSA decryption is achieved via modular exponentiation and their protocol is based on the Chinese Remainder Theorem (CRT) as proposed in 1982 by Quisquater and Couvreur [12].…”

Section: Introductionmentioning

confidence: 99%

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Comment on “Efficient and Secure Outsourcing Scheme for RSA Decryption in Internet of Things”

Vergnaud

2020

IEEE Internet Things J.

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Internet of Things (IoT) devices have grown in popularity over the past few years. The RSA public-key cryptographic primitive is time-consuming for resource-constrained IoT. Recently, Zhang, Yu, Tian, Tong, Lin, Ge and Wang proposed a two-party outsourcing protocol between a client and a server for RSA decryption in IoT. It relies on the Chinese Remainder Theorem as proposed by Quisquater and Couvreur in 1982 and is very efficient.We show that their protocol does not achieve the claimed security guarantees: (1) the (secret) decryption exponent, the plaintext and the factorization of the RSA modulus are revealed to a passive adversary, and (2) a malicious server can make the client accept an (invalid) value of its choice as the result of the delegated computation.

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A Hybridized Machine Learning Model for Optimal Feature Selection and Attack Detection in Cloud SaaS Framework

Saisindhutheja

Shyam

2021

IoT and Analytics for Sensor Networks

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Efficient and Secure Outsourcing Scheme for RSA Decryption in Internet of Things

Cited by 20 publications

References 40 publications

A Novel Community Detection Algorithm Based on Paring, Splitting and Aggregating in Internet of Things

A Novel Community Detection Algorithm Based on Paring, Splitting and Aggregating in Internet of Things

Comment on “Efficient and Secure Outsourcing Scheme for RSA Decryption in Internet of Things”

A Hybridized Machine Learning Model for Optimal Feature Selection and Attack Detection in Cloud SaaS Framework

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