CPP-ELM: Cryptographically Privacy-Preserving Extreme Learning Machine for Cloud Systems

Çatak, Ferhat Özgür; Mustaçoğlu, Ahmet Fatih

doi:10.2991/ijcis.11.1.3

Cited by 28 publications

(13 citation statements)

References 27 publications

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“…To protect user attribute privacy, a sequence of privacy protection schemes have been presented [16,17]. Generally, policy hiding consists of two types: partial hiding and full hiding.…”

Section: Related Workmentioning

confidence: 99%

Sharing and Privacy in PHRs: Efficient Policy Hiding and Update Attribute-based Encryption

Liu¹,

Ji²,

Yin³

et al. 2021

KSII TIIS

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Personal health records (PHRs) is an electronic medical system that enables patients to acquire, manage and share their health data. Nevertheless, data confidentiality and user privacy in PHRs have not been handled completely. As a fine-grained access control over health data, ciphertext-policy attribute-based encryption (CP-ABE) has an ability to guarantee data confidentiality. However, existing CP-ABE solutions for PHRs are facing some new challenges in access control, such as policy privacy disclosure and dynamic policy update. In terms of addressing these problems, we propose a privacy protection and dynamic share system (PPADS) based on CP-ABE for PHRs, which supports full policy hiding and flexible access control. In the system, attribute information of access policy is fully hidden by attribute bloom filter. Moreover, data user produces a transforming key for the PHRs Cloud to change access policy dynamically. Furthermore, relied on security analysis, PPADS is selectively secure under standard model. Finally, the performance comparisons and simulation results demonstrate that PPADS is suitable for PHRs.

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“…To protect user attribute privacy, a sequence of privacy protection schemes have been presented [16,17]. Generally, policy hiding consists of two types: partial hiding and full hiding.…”

Section: Related Workmentioning

confidence: 99%

Sharing and Privacy in PHRs: Efficient Policy Hiding and Update Attribute-based Encryption

Liu¹,

Ji²,

Yin³

et al. 2021

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…In our previous work, [7], we applied partially homomorphic encryption methods to build a probabilistic classifier using the extreme learning machine algorithm. Within the scope of this previous study, we have examined how to create classification models using homomorphic encryption algorithms.…”

Section: Contributionmentioning

confidence: 99%

Practical Implementation of Privacy Preserving Clustering Methods Using a Partially Homomorphic Encryption Algorithm

et al. 2020

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The protection and processing of sensitive data in big data systems are common problems as the increase in data size increases the need for high processing power. Protection of the sensitive data on a system that contains multiple connections with different privacy policies, also brings the need to use proper cryptographic key exchange methods for each party, as extra work. Homomorphic encryption methods can perform similar arithmetic operations on encrypted data in the same way as a plain format of the data. Thus, these methods provide data privacy, as data are processed in the encrypted domain, without the need for a plain form and this allows outsourcing of the computations to cloud systems. This also brings simplicity on key exchange sessions for all sides. In this paper, we propose novel privacy preserving clustering methods, alongside homomorphic encryption schemes that can run on a common high performance computation platform, such as a cloud system. As a result, the parties of this system will not need to possess high processing power because the most power demanding tasks would be done on any cloud system provider. Our system offers a privacy preserving distance matrix calculation for several clustering algorithms. Considering both encrypted and plain forms of the same data for different key and data lengths, our privacy preserving training method’s performance results are obtained for four different data clustering algorithms, while considering six different evaluation metrics.

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“…However, most of the transmission processes for these contents have been done through an unsecure network, which means there is potential risk for the loss of information or the interception (i.e., illegal copying and distribution) or even malicious tampering of this information [1,2]. Therefore, the security of multimedia information, especially in the field of big data and cloud systems, has raised increasing concern [3,4]. Traditional encryption methods include the implementation of the data encryption standard (DES) and advance encryption standard (AES) algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Calculate the initial conditions µ, λ, x 0 , and y 0 in accordance with the h 1 , h 2 , h 3 , h 4 , and the equations applied are displayed in (3).…”

mentioning

confidence: 99%

A Novel Asymmetric Hyperchaotic Image Encryption Scheme Based on Elliptic Curve Cryptography

et al. 2021

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Most of the image encryption schemes based on chaos have so far employed symmetric key cryptography, which leads to a situation where the key cannot be transmitted in public channels, thus limiting their extended application. Based on the elliptic curve cryptography (ECC), we proposed a public key image encryption method where the hash value derived from the plain image was encrypted by ECC. Furthermore, during image permutation, a novel algorithm based on different-sized block was proposed. The plain image was firstly divided into five planes according to the amount of information contained in different bits: the combination of the low 4 bits, and other four planes of high 4 bits respectively. Second, for different planes, the corresponding method of block partition was followed by the rule that the higher the bit plane, the smaller the size of the partitioned block as a basic unit for permutation. In the diffusion phase, the used hyperchaotic sequences in permutation were applied to improve the efficiency. Lots of experimental simulations and cryptanalyses were implemented in which the NPCR and UACI are 99.6124% and 33.4600% respectively, which all suggested that it can effectively resist statistical analysis attacks and chosen plaintext attacks.

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CPP-ELM: Cryptographically Privacy-Preserving Extreme Learning Machine for Cloud Systems

Cited by 28 publications

References 27 publications

Sharing and Privacy in PHRs: Efficient Policy Hiding and Update Attribute-based Encryption

Sharing and Privacy in PHRs: Efficient Policy Hiding and Update Attribute-based Encryption

Practical Implementation of Privacy Preserving Clustering Methods Using a Partially Homomorphic Encryption Algorithm

A Novel Asymmetric Hyperchaotic Image Encryption Scheme Based on Elliptic Curve Cryptography

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