Abstract:
The competing parties who have private data may collaboratively conduct privacy preserving distributed data analysis (PPDA) tasks to learn beneficial data models or analysis results. For example , different credit card companies may try to build better models for credit card fraud detection through PPDA tasks. Similarly, competing companies in the same industry may try to combine their sales data to build models that may predict the future sales. In many of these cases, the competing parties have different incentives. Although certain PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is impossible to verify whether or not participating parties are truthful about their private input data. In other words, unless proper incentives are set, even current PPDA techniques cannot prevent participating parties from modifying their private inputs. This raises the question of how to design incentive compatible privacy-preserving data analysis techniques that motivate participating parties to provide truthful input data. In this paper, we first develop key theorems, then base on these theorem, we analyze what types of privacy-preserving data analysis tasks could be conducted in a way that telling the truth is the best choice for any participating party.
Existing system:
Even though privacy-preserving data analysis techniques guarantee that nothing other than the final result is disclosed, whether or not participating parties provide truthful input data cannot be verified. Although certain PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is impossible to verify whether or not participating parties are truthful about their private input data. In other words, unless proper incentives are set, even current PPDA techniques cannot prevent participating parties from modifying their private inputs.
DisAdvatages:
- participating parties Can’t provide truthfull input data.
- Security system not depend upon the truthfull data.
- Fraud Accecpt the Credit card easily.
Proposed System:
In design incentive compatible privacy-preserving data analysis techniques that motivate participating parties to provide truthful input data. In this paper, we first develop key theorems, then base on these theorem, we analyze what types of privacy-preserving data analysis tasks could be conducted in a way that telling the truth is the best choice for any participating party. Secure multi-party computation (SMC) has recently emerged as an answer to this problem.
Advantages:
1.Users give their truthfull data for security system.
2.User Only Knows the answers for security questions.
3.Users Knows the Fraud entry.
4.Fraud could be detected.
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Web Technologies : HTML, CSS, JS. JSP
• Programming Language : Java and J2EE
• Database Connectivity : JDBC
• Backend Database : MySQL
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
- Processor : Core I3
- RAM Capacity : 2 GB
- Hard Disk : 250 GB
- Monitor : 15″ Color
- Mouse : Two or Three Button Mouse
- Key Board : Windows 08/10
