Adaptive Random Decision Tree: A New Approach for Data Mining with Privacy Preserving

2015 ◽  
Vol 03 (07) ◽  
pp. 6378-6384
Author(s):  
Hemlata B. Deorukhakar, Prof. Pradnya Kasture
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Privacy Preserving ◽  
New Approach

scholarly journals Securely Outsourcing ID3 Decision Tree in Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Ye Li ◽  
Zoe L. Jiang ◽  
Xuan Wang ◽  
Junbin Fang ◽  
En Zhang ◽  
...  
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Decision Tree ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Security Model ◽  
Garbled Circuits ◽  
Private Data ◽  
Malicious Model ◽  
And Performance

With the wide application of Internet of Things (IoT), a huge number of data are collected from IoT networks and are required to be processed, such as data mining. Although it is popular to outsource storage and computation to cloud, it may invade privacy of participants’ information. Cryptography-based privacy-preserving data mining has been proposed to protect the privacy of participating parties’ data for this process. However, it is still an open problem to handle with multiparticipant’s ciphertext computation and analysis. And these algorithms rely on the semihonest security model which requires all parties to follow the protocol rules. In this paper, we address the challenge of outsourcing ID3 decision tree algorithm in the malicious model. Particularly, to securely store and compute private data, the two-participant symmetric homomorphic encryption supporting addition and multiplication is proposed. To keep from malicious behaviors of cloud computing server, the secure garbled circuits are adopted to propose the privacy-preserving weight average protocol. Security and performance are analyzed.

scholarly journals A Novel Privacy Preserving Data mining using improved decision tree and KP-ABE on High Dimensional Data

2018 ◽  
Vol 7 (2.7) ◽  
pp. 515
Author(s):  
Aaluri Seenu ◽  
M Kameswara Rao
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Classification Model ◽  
Distributed Data Mining ◽  
High Dimensional ◽  
Distributed Data ◽  
Privacy Preserving Data Mining ◽  
Tree Classifier

In distributed data mining environment maintaining individual data or patterns is a major issue due to high dimensionality and data size. Distributed Data mining framework can help to find the essential decision making patterns from distributed data. Privacy preserving data mining (PPDM) has emerged as a main research area for data confidentiality and knowledge sharing in between the communicating parties. As the distributed data of the individuals are stored by the third party, it leads to the misuse of distributed information in digital networks. Most of the decision patterns generated using the machine learning models for business organizations, industries and individuals has to be encoded before it is publicly shared or published. As the amount of data collected from different sources are increasing exponentially, the time taken to preserve the patterns using the  traditional privacy preserving data mining models also increasing due to high computational attribute selection measures and noise in the distributed data. Also, filling sparse values using the conventional models are inefficient and infeasible for privacy preserving models. In this paper, a novel privacy preserving based classification model was designed and implemented on large datasets. In this model, a filter-based privacy preserving model using improved decision tree classifier is implemented to preserve the decision patterns using IPPDM-KPABE model. Experimental results proved that the proposed model has high computational efficiency compared to the traditional privacy preserving model on high dimensional datasets. 

scholarly journals Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 103 ◽  
Author(s):  
Lin Liu ◽  
Jinshu Su ◽  
Baokang Zhao ◽  
Qiong Wang ◽  
Jinrong Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Decision Tree ◽  
Secret Sharing ◽  
Cloud Model ◽  
Privacy Preserving ◽  
The Internet ◽  
Fast Development ◽  
The Internet Of Things ◽  
Tree Evaluation

With the fast development of the Internet of Things (IoT) technology, normal people and organizations can produce massive data every day. Due to a lack of data mining expertise and computation resources, most of them choose to use data mining services. Unfortunately, directly sending query data to the cloud may violate their privacy. In this work, we mainly consider designing a scheme that enables the cloud to provide an efficient privacy-preserving decision tree evaluation service for resource-constrained clients in the IoT. To design such a scheme, a new secure comparison protocol based on additive secret sharing technology is proposed in a two-cloud model. Then we introduce our privacy-preserving decision tree evaluation scheme which is designed by the secret sharing technology and additively homomorphic cryptosystem. In this scheme, the cloud learns nothing of the query data and classification results, and the client has no idea of the tree. Moreover, this scheme also supports offline users. Theoretical analyses and experimental results show that our scheme is very efficient. Compared with the state-of-art work, both the communication and computational overheads of the newly designed scheme are smaller when dealing with deep but sparse trees.

Research on Privacy Preserving Classification Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1863-1867 ◽  
Author(s):  
Xun Yi Ren ◽  
Wu Yuan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Classification Algorithm ◽  
Decision Tree Classification ◽  
Private Data

In the process of data mining, how to operate the data mining as well as protect the private data is a problem must be solved. This paper proposed an improvement of decision tree classification algorithm. Homomorphism encryption system, digital envelopes technology and secret sorting are applied to protect the data privacy. Our contribution is a privacy preserving protocol consist of homomorphism encryption system and secret sorting. Analysis shows that this algorithm can get right results as well as protect the privacy of the private data.

Decision Trees and Random Forest for Privacy-Preserving Data Mining

2013 ◽  
pp. 71-90 ◽  
Author(s):  
Gábor Szucs
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Decision Tree ◽  
Decision Trees ◽  
Privacy Preserving ◽  
Randomized Response Technique ◽  
Random Response ◽  
Randomized Response ◽  
Classification Problems ◽  
Privacy Preserving Data Mining

The objective of this chapter is to present brief literature and new results of research in privacy-preserving data mining as an important privacy issue in the e-business area. The chapter focuses on classification problems in business analytics, where the enterprises can gain large profit using predicted results by classification. The decision tree is a well-known classification technique, and its modification by the Randomized Response technique is described for privacy-preserving data mining. This algorithm is developed for all types of attributes. The largest contribution of this chapter is a new method, so called Random Response Forest, consisting of many decision trees and a randomization technique. Random Response Forest is similar to Random Forest, but it is able to solve privacy problems. This consists of many shallow trees, where a shallow tree is a special decision tree with a Randomized Response technique, and the precision of Random Response Forest is better than at a tree.

scholarly journals New Improved Algorithm for Mining Privacy - Preserving Frequent Itemsets

2011 ◽  
pp. 1-7
Author(s):  
Rashmi Awasthy ◽  
Rajesh Shrivastava ◽  
Bharat Solanki
Keyword(s):  
Data Mining ◽  
Research Area ◽  
Privacy Preserving ◽  
Frequent Itemsets ◽  
Important Research ◽  
New Approach ◽  
Large Databases ◽  
Very Large Databases ◽  
Important Research Area ◽  
Transaction Database

Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions is evolving into an important research area. Frequent Itemsets (FI) Mining is one of the most researched areas of data mining. In order to mining privacy preserving frequent itemsets on large transaction database efficiently, a new approach was proposed in this paper.

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