Privacy Preserving Distributed Data Fusion Based On Attribute Protection
ABSTARCT :
Privacy-preserving distributed data fusion is a pretreatment process in data mining involving security models. In this paper, we present a method of implementing multiparty data fusion, wherein redundant attributes of a same set of individuals are stored by multiple parties. In particular, the merged data does not suffer from background attacks or other reasoning attacks, and individual attributes are not leaked. To achieve this, we present three algorithms that satisfy K-anonymous and differential privacy. Experimental results on real datasets suggest that the proposed algorithm can effectively preserve information in data mining tasks.
EXISTING SYSTEM :
? Cyber Attack is a type of cyber threats that is targeting to get private information such as credit cards information and social security numbers.
? De-synchronization Attack is the pseudonym stored at the gateway node GW and the sensor node SN memory would not be the same, because of an adversary blocks the communication between the parties.
? There is not a specific solution that can detect whole phishing
? The purpose of this study is to perform Extreme Learning Machine (ELM) based classification for 30 features including Websites Data in UC Irvine Machine Learning Repository database.
? For results assessment, ELM was compared with other machine learning methods such as Support Vector Machine (SVM).
DISADVANTAGE :
? Internet is an essential part of our life. Internet users can be affected from different types of cyber threats.
? Thus cyber threats may attack ?nancial data, private information, online banking and e-commerce.
? Sql Injection
? Dos Attack
? Password Attack
PROPOSED SYSTEM :
• In this study, features in the database created for Cyber Attacks are classified by determining the input and output parameters for the Support Vector machine classifier.
• Results obtained by SVM show that has higher achievement compared to other classifier methods.
ADVANTAGE :
? The proposed methodology imports data-set of Privacy Information and Image Based Password Authentication from the database and then the imported data is pre-processed.
? The Attackers url will be blocked based on request from hackers server.
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