Paper Title
Application of PSO Optimization Technique on Medical Data to Uphold Data Privacy

Data privacy is an emerging research topic, as security issues are on high rise everywhere. Even though, security guidelines, policies, laws and acts are in force, data hackers are on their way to steal the data. Data hackers are not only competent in thefting the data but also experts in analysis. At the same time, there is advancement in technology, to counter data hacking. Generally, medical data contain sensitive information. Hackers use this underlying sensitive information and do fraudulent activities. To protect the confidentiality of the data, the researchers have undertaken design and development of an algorithm with the optimization technique to improve the accuracy of privacy on medical data. As per the researchers view, data privacy can be protected through cryptography, and data access controls. Optimization techniques are outweighing the data privacy implementations. The current paper describes self-defined algorithm on medical data (heart data sets), to improve the privacy accuracy on three openly available data sets, applying PSO optimization technique. The current experiment includes standard privacy techniques: clustering, encryption and distribution. In this work, accuracy is measured on varying number of clusters ranging from 2 to 10, on each of the data sets. The paper describes methodology, PSO optimization, and implementation and result evaluation. Index Terms— Data privacy, Privacy Preserving Data Mining (PPDM), PSO