Secure kNN Query Processing in Untrusted Cloud Environments: an Overview

ABSTRACT: Nowadays, data are stored to a third party in cloud environments and query processing is also done by the third party to reduce the expense to maintain the system. Although there are lots of advantages in using independent third parties in query processing, security problems become more crucial since we cannot completely trust the third parties which can be easily corrupted or malfunctioning. The security problems with untrusted third parties are multifaceted in several areas such as privacy, authentication, and recovery. For privacy, the third party should not be able to know what the user's query is since the query itself describes the user's interest. For authentication, the user should be able to verify that the information from the third party is not tampered since the correctness of the query results depends upon the correctness of the information from the third party. For recovery, when the result is found to be forged by an adversary, we should be able to find the adversary and get a correct result by removing the adversary. To address these challenges, we propose several schemes. First, with respect to secure kNN query processing and secure proximity detection, we give novel schemes based on Mutable Order Preserving Encryption (MOPE) and Secure Point Evaluation Method (SPEM). Second, for authenticated top-k aggregation, we suggest novel schemes using Three Phase Uniform Threshold Algorithm, Merkle Hash Tree, and Condensed-RSA. Third, for detecting malicious nodes, we propose novel algorithms based on Additively Homomorphic Encryption and Multipath Transmission. Our experimental evaluation and security analyses demonstrate that robust mechanisms can be deployed with a minimal amount of computational and communicational expense

INFREQUENT WEIGHTED ITEMSET MINING FOR TRANSACTIONAL DATABASES USING FREQUENT PATTERN GROWTH

Mining Weighted Item sets from a transactional database includes to the discovery of itemsets with high utility like profits.Although a number of relevant techniques have been planned in recent years, they obtain the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets weakens the mining performance in terms of execution time and space requirement. In this paper we have concentrate on UP-Growth and UP-Growth+ algorithmwhich will overcome this impediment. This technique includes tree based data structure finding itemsets, UP-Tree for generating candidate itemsets with two scan of database. In this paper we extend the functionality of UP-Growth and UP-Growth+ algorithms on transactional database. The situation may become poorwhen the database contains lots of long transactions or long high utility itemsets. An appearing topic in the field of data mining is utility mining. The main goal of utility mining is to identify the itemsets with highest utilities, by considering profit, quantity, cost or other user preferences. This topic includes many applications in website click stream analysis, business promotion in chain hypermarkets, cross marketing in retail stores, online e-commerce management, and mobile commerce environment planning and even finding important patterns in biomedical applications

A Survey on Access Control and Privacy Preserving Mechanisms on Relational Database

ABSTRACT In current days of information technology, the assessment of data from existing database that are connected in a network were carried out simply by applying protection to database at the time of access only. Since during access of such data the access or privacy protection will not be there, due to this release and leakage of data can occur in a network. While the Access protection mechanism provides protection to sensitive data and privacy protection mechanism avoid the assessment of data by unauthorized users. Here the introductory and literature survey has presented which help further to design efficient system by considering all terms and circumstances