Preserving Privacy Using Attribute Based Access Control in DataStream

Abstract

Data security is a very broad area that addresses many issues, like legal and ethical issues regarding the right to access certain information. The sensitive data is accessible to authorized users only. The database security is based on the Access Control Mechanism (ACM) and the Privacy Protection Mechanism (PPM). The Access Control Mechanisms (ACM) is used to ensure that only information is available authorized to users. The authorized user can only access the authorized data. The privacy protection mechanism (PPM) is a general method used to transform the original data into some anonymous form to prevent from accessing owners sensitive information. There are numerous methods to provide the privacy for the sensitive data. In existing literature, the Access Control mechanism allows Role Based Access using Time Based Sliding Window Query. It protects the user information from the unauthorized access. PPM meets privacy requirement through k-anonymity it provides better privacy for the sensitive information which is to be shared. The privacy is achieved by the high accuracy of the user information. To protect data, the anonymization method is one of the best privacy protection mechanisms. The anonymization process will transform the sensitive information to some anonymzed form using K-anonymity, â„“ -diversity. The PPM needs to satisfy an additional constraint namely the Imprecision bound for each selection predicate. The imprecision bound reduced the delaying for publishing data stream. The challenge is to optimize the delay in publishing data stream, if the delay in publishing query is reduced then the imprecision bound is satisfied. Proposed system is an accuracy based access control using Attribute Based Access Control with Tuple Based Sliding Window Query. and PPM with the â„“ -diversity. The â„“ -diversity method is an extension of the k-anonymity method, it is more efficient than the k-anonymity method. It avoids the attacks like background knowledge attack and others in k-anonymity method.