Efficient paillier cryptoprocessor for privacy‐preserving data mining

This paper is about an encryption based approach to the secure implementation of feedback controllers for physical systems. Specifically, Paillier's homomorphic encryption is used to digitally implement a class of linear dynamic controllers, which includes the commonplace static gain and PID type feedback control laws as special cases. The developed implementation is amenable to Field Programmable Gate Array (FPGA) realization. Experimental results, including timing analysis and resource usage characteristics for different encryption key lengths, are presented for the realization of an inverted pendulum controller; as this is an unstable plant, the control is necessarily fast.

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“…Using this property makes our implementation of the encryption considerably faster than [47]. Decryption of a ciphertext c is done by calculating…”

Section: Homomorphic Encryptionmentioning

confidence: 99%

Implementing homomorphic encryption based secure feedback control

Tran¹,

Farokhi

Cantoni

et al. 2020

Control Engineering Practice

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“…Paillier cryptosystem is widely used as a homomorphic encryption plan to guarantee privacy necessities. Ismail San et al [10] explore how to handle execution corruption of Paillier cryptosystem. To begin with, use parallelism between tasks in cryptosystem and interleaving among free activities.…”

Section: Literature Surveymentioning

confidence: 99%

Implementation of Client Controlled Privacy Preserving Data Model for Mining Decision Rules Using Decision Tree and Association Rules

Ilyas*,

Verma,

Deshpande

et al. 2019

IJITEE

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The privacy-preserving data mining (PPDM) is one of the techniques which are used for mining data dynamically with preserving privacy of the end data owner. In this paper, a PPDM technique for generating the privacy-preserving decision rules is proposed and implemented. The key motive of presenting this privacy-preserving decision rule mining technique is to demonstrate how securely data is aggregated in the PPDM environment, how securely extract them and consumed them with the help of applications. In addition to comparing the state of art methods for mining privacy preserving decision rules for preparing the future directions of research. Therefore two different data models have used namely decision tree and association rule mining. The conducted experiments demonstrate that decision tree-based techniques are superior to the association rule mining based techniques for mining higher dimensional data with higher accuracy and low resource consumption. Therefore in the near future for extending this data model the two concepts are also introduced in this paper.

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“…Li and Jung [5] designed a suite of fine-grained privacy-preserving location query protocols (PLQP) by applying Paillier’s cryptosystem [32,33]. It can solve the privacy issues in existing LBS applications.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Location-Based Service Scheme for Mobile Sensing Data

Xie

Wang

2016

Sensors

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With the wide use of mobile sensing application, more and more location-embedded data are collected and stored in mobile clouds, such as iCloud, Samsung cloud, etc. Using these data, the cloud service provider (CSP) can provide location-based service (LBS) for users. However, the mobile cloud is untrustworthy. The privacy concerns force the sensitive locations to be stored on the mobile cloud in an encrypted form. However, this brings a great challenge to utilize these data to provide efficient LBS. To solve this problem, we propose a privacy-preserving LBS scheme for mobile sensing data, based on the RSA (for Rivest, Shamir and Adleman) algorithm and ciphertext policy attribute-based encryption (CP-ABE) scheme. The mobile cloud can perform location distance computing and comparison efficiently for authorized users, without location privacy leakage. In the end, theoretical security analysis and experimental evaluation demonstrate that our scheme is secure against the chosen plaintext attack (CPA) and efficient enough for practical applications in terms of user side computation overhead.

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Efficient paillier cryptoprocessor for privacy‐preserving data mining

Cited by 27 publications

References 32 publications

Implementing homomorphic encryption based secure feedback control

Implementing homomorphic encryption based secure feedback control

Implementation of Client Controlled Privacy Preserving Data Model for Mining Decision Rules Using Decision Tree and Association Rules

Privacy-Preserving Location-Based Service Scheme for Mobile Sensing Data

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