Privacy Preserving Data Mining Using Radial Basis Functions on Horizontally Partitioned Databases in the Malicious Model

2014 ◽  
Vol 23 (05) ◽  
pp. 1450007 ◽  
Author(s):  
Alexandros Panteli ◽  
Manolis Maragoudakis ◽  
Stefanos Gritzalis
Keyword(s):  
Rbf Neural Network ◽  
Privacy Preserving ◽  
User Model ◽  
Basis Functions ◽  
Third Party ◽  
Communication Overhead ◽  
The Novel ◽  
Privacy Preserving Data Mining ◽  
Radial Basis ◽  
Malicious Model

This paper presents a privacy preserving protocol for the computation of a Radial Basis Function (RBF) neural network model between N participants which share horizontally partitioned datasets. The RBF model is used for regression analysis tasks. The novel aspect of the proposed protocol lies to the fact that it assumes a malicious user model and does not use homomorphic cryptographic methods, which are inherently only suited for a semi-trusted user environment. The performance analysis shows that the communication overhead is low enough to warranty its use while the computational complexity is identical in most cases with the centralized computation scenario (e.g. a trusted third party). The accuracy of the output model is only marginally subpar to a centralized computation on the union of all datasets.

scholarly journals A Novel Privacy-Preserving Mobile-Coverage Scheme Based on Trustworthiness in HWSNs

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chunyang Qi ◽  
Jie Huang ◽  
Bin Wang ◽  
Hongkai Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Data Transmission ◽  
Discrete Logarithm ◽  
Privacy Preserving ◽  
Wireless Sensor ◽  
Communication Overhead ◽  
The Novel ◽  
Network Coverage ◽  
Pollution Attacks ◽  
Previous Algorithm

To solve the problem of security deployment in a hybrid wireless sensor network, a novel privacy-preserving mobile coverage scheme based on trustworthiness is proposed. The novel scheme can efficiently mitigate some malicious attacks such as eavesdropping and pollution and optimize the coverage of hybrid wireless sensor networks (HWSNs) at the same time. Compared with the traditional mobile coverage scheme, the security of data transmission and mobility are considered in the deployment of HWSNs. Firstly, our scheme can mitigate the eavesdropping attacks efficiently utilizing privacy-preserving signature. Then, the trust mobile protocol based on the trustworthiness is used to defend the pollution attacks and improve the security of mobility. In privacy-preserving signature, the hardness of discrete logarithm determines the degree of security of the privacy-preserving signature. The correctness and effectiveness of signature algorithm are proven by the probabilities of the native messages which can be recovered and forged which is negligible. Furthermore, a mobile scheme based on the trustworthiness (MSTW) is proposed to optimize the network coverage and improve the security of mobility. Finally, the simulation compared with a previous algorithm is carried out, in which the communication overhead, computational complexity, and the coverage are given. The result of the simulation shows that our scheme has roughly the same network coverage as the previous schemes on the basis of ensuring the security of the data transmission and mobility.

scholarly journals Decentralized Privacy-Preserving Data Aggregation Scheme for Smart Grid Based on Blockchain

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5282 ◽  
Author(s):  
Hongbin Fan ◽  
Yining Liu ◽  
Zhixin Zeng
Keyword(s):  
Smart Grid ◽  
Data Aggregation ◽  
Privacy Preserving ◽  
Third Party ◽  
Security And Privacy ◽  
Communication Overhead ◽  
User Privacy ◽  
Grid Data ◽  
Trusted Third Party ◽  
Grid Based

As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. Smart meters face serious security challenges, such as a trusted third party or a trusted authority being attacked, which leads to the disclosure of user privacy. Blockchain provides a viable solution that can use its key technologies to solve this problem. Blockchain is a new type of decentralized protocol that does not require a trusted third party or a central authority. Therefore, this paper proposes a decentralized privacy-preserving data aggregation (DPPDA) scheme for smart grid based on blockchain. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user’s power consumption data. Boneh-Lynn-Shacham short signature and SHA-256 function are applied to ensure the confidentiality and integrity of user data, which is convenient for billing and power regulation. The scheme protects user privacy data while achieving decentralization, without relying on TTP or CA. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.

Privacy-Preserving Data Mining

2009 ◽  
pp. 527-536 ◽  
Author(s):  
Alexandre Evfimievski ◽  
Tyrone Grandison
Keyword(s):  
Data Mining ◽  
Privacy Preservation ◽  
Statistical Processing ◽  
Privacy Preserving ◽  
Third Party ◽  
Data Publishing ◽  
Sensitive Information ◽  
Public Servants ◽  
Data Set ◽  
Privacy Preserving Data Mining

Privacy-preserving data mining (PPDM) refers to the area of data mining that seeks to safeguard sensitive information from unsolicited or unsanctioned disclosure. Most traditional data mining techniques analyze and model the data set statistically, in aggregated form, while privacy preservation is primarily concerned with protecting against disclosure of individual data records. This domain separation points to the technical feasibility of PPDM. Historically, issues related to PPDM were first studied by the national statistical agencies interested in collecting private social and economical data, such as census and tax records, and making it available for analysis by public servants, companies, and researchers. Building accurate socioeconomical models is vital for business planning and public policy. Yet, there is no way of knowing in advance what models may be needed, nor is it feasible for the statistical agency to perform all data processing for everyone, playing the role of a trusted third party. Instead, the agency provides the data in a sanitized form that allows statistical processing and protects the privacy of individual records, solving a problem known as privacypreserving data publishing. For a survey of work in statistical databases, see Adam and Wortmann (1989) and Willenborg and de Waal (2001).

scholarly journals Privacy Preserving Data Mining for Pharmacovigilance

2019 ◽  
Vol 9 (1S) ◽  
pp. 78-80
Keyword(s):  
Data Mining ◽  
Adverse Effect ◽  
Adverse Events ◽  
Visual Cryptography ◽  
Privacy Preserving ◽  
Specific Drug ◽  
The Novel ◽  
Potential Danger ◽  
Sensitive Data ◽  
Privacy Preserving Data Mining

In this paper, visual cryptography concept is used for preserving the privacy of sensitive data used for pharmacovigilance. Overall analysis of adverse events of a specific drug helps in finding the potential danger of using the a specific drug. Preserving data owners privacy is done by using visual cryptography technique. Tetracycline drugs adverse effect present in FAERS dataset is extracted, encrypted and decrypted by the novel methodology proposed.

AUTOMATIC DETERMINATION OF RADIAL BASIS FUNCTIONS: AN IMMUNITY-BASED APPROACH

2001 ◽  
Vol 11 (06) ◽  
pp. 523-535 ◽  
Author(s):  
LEANDRO NUNES DE CASTRO ◽  
FERNANDO J. VON ZUBEN
Keyword(s):  
Radial Basis Functions ◽  
Rbf Neural Network ◽  
Basis Functions ◽  
Classification Problems ◽  
Selection Procedures ◽  
Rbf Network ◽  
Radial Basis ◽  
Optimal Set ◽  
Unsupervised Cluster Analysis

The appropriate operation of a radial basis function (RBF) neural network depends mainly upon an adequate choice of the parameters of its basis functions. The simplest approach to train an RBF network is to assume fixed radial basis functions defining the activation of the hidden units. Once the RBF parameters are fixed, the optimal set of output weights can be determined straightforwardly by using a linear least squares algorithm, which generally means reduction in the learning time as compared to the determination of all RBF network parameters using supervised learning. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBFs. The approach proposed here is inspired by models derived from the vertebrate immune system, that will be shown to perform unsupervised cluster analysis. The algorithm is introduced and its performance is compared to that of the random, k-means center selection procedures and other results from the literature. By automatically defining the number of RBF centers, their positions and dispersions, the proposed method leads to parsimonious solutions. Simulation results are reported concerning regression and classification problems.

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