International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 02 Issue: 03 | June-2015 www.irjet.net p-ISSN: 2395-0072 © 2015, IRJET.NET- All Rights Reserved Page 86 BOOSTING THE PRIVACY OF REAL TIME DATA WITH DIFFERENTIAL PRIVACY Ms.Neeta Patil 1 , Prof.Pankaj Agarkar 2 1 PG Student, Computer Engineering, Dr.D.Y.Patil School of Engineering,Lohgaon,Pune,India 2 Assistant Professor, Computer Engineering, Dr.D.Y.Patil School of Engineering,Lohgaon,Pune,India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Performing data mining of real time data has great value. Generally it has importance in real time applications like traffic congestion applications, disease surveillance applications. In these types of applications data values are highly correlated so providing differential privacy to this data is essential. Hence while maintaining the confidentiality of data, the purpose of statistical database is to allow third parties or users to study the properties of traffic congestion applications or contents in the databases but individual users contribution is keep safe from damage. Trusted party carry the whole database and provide requested information to third party in the form of count. User or third party make query to the trusted party for accessing some data from trusted server. In the form of aggregate function trusted server or party sends the requested data count to user. This aggregate function is formed as answer to the query plus some random noise. So to provide strong privacy guarantee all appreciable data is examine methodically. Key Words: Differential privacy, Geometric mechanism, Exponential mechanism, Laplace perturbation, filtering, sampling 1. INTRODUCTION While considering some applications sharing of real time data has great value. Because many times such data is private data and needs to maintain privacy of this data. For that purpose real time data is mostly shared in the form of aggregate function which provide count to user. When real time data is shared in the aggregate function form security of that data is maintained properly. Thus to provide strong guarantee about confidentiality of data all appreciable data is examine methodically. Over the past few years, privacy of data is preserved by maintaining some terms. Privacy is provided to databases, the general principles of science(theory) and other kind of data. This privacy is provided by means to increase the correctness of queries from statistical databases while decreasing opportunity of identifying its records (differential privacy). In many data mining applications, real time data is provided in the form of aggregate function to provide the security to private data. Applications where such privacy can be provided are Disease Surveillance, monitoring of road traffic, medical records, voter registration information, and email usage. The goal of this differential privacy is analysis of confidential data by preserving the privacy of data. Consider following examples: Disease Surveillance: Providers of health care collects data from individual visitors. After that all this collected data is shared with third party, who maintains record of this data properly and securely. This third party has all the collected data. So third party may share this data to other parties. Other parties are may be trusted or not. Monitoring of traffic: Providers of GPS service collect data from individual users. This data is related to location, speed of vehicle and mobility. Again here providers of GPS service share data with third party which is trusted or not. In recent years lot of research is done on privacy preserving of whatever data get published. Different terms or concepts are there to preserve the privacy of statistical databases. Among those differential privacy has lots of importance. As different applications like disease surveillance, bank database or other maintain lots of data in their databases. This data needs to collect and then stored. All stored data is maintained properly so that guarantee about privacy is provided. So preserving privacy of secure data is big question in front of different organizations and government. Attacker can do any type of attack to access the data from database. While making access of data available to user, strong privacy guarantee needs to provide. Trusted party done not have attackers background knowledge. 2. LITERATURE SURVEY 2.1 Calibrating Noise Trusted servers carry the database or private information of individual users. User make query to the trusted server for accessing some data. Trusted server sends the requested data to user in the form of aggregate function.. This aggregate function is formed as answer to the query plus some random noise. So to provide strong privacy guarantee all appreciable data is examine methodically. There are two basic categories for privacy i.e. input and output perturbation: In first approach, data are changed randomly and answer to the user created query is calculated from changed data. In second approach i.e. output perturbation, using real data correct answer to the query is calculated and noisy data is provided.