Study of Incentive Compatible Privacy Preserving Data Analysis - Singh, Yuvraj; Singh, Pankaj Pratap; Kumar Tripathi, Anirudh; kishor, Amit (International Journal of Computer Sciences and Engineering, 2019)
In corporate and government department’s increasingly keeping large size electronic databases, which are accessed using internet or intranet. Important information implement from the data using Privacy data mining methods. While performing data mining steps, there is an inherent danger to the privacy of the data. The valuable data stored in the database should not be accessible to users. Most of the privacy preserving methods are based on reduction in the granularity of the implementing of the data. This ends to loss of information but it improves privacy. Therefore, in PPDM there is a conflict between loss of information and the privacy. Effective Methods are required which do not compromise the security mechanisms. Some of the methods proposed for privacy preserving data mining include randomization method, k-anonymity model, l-diversity and distributed privacy preservation. The k-anonymity model is based on a quasi-identifier, which is a collection of attributes in a database that is the identifier for the entire data. All the data in the database is assumed to be in a set of tables, and each tuple is information of an individual customer. K-anonymity Methods are based on the reduction of granularity in representation of data using pseudo identifiers. Major Methods used for granularity reduction are generalization and suppression. In generalization, the attribute values are converted into a range that reduces the granularity and reduces the risk of identifying individual values. In suppression, value of the attribute is removed completely. These methods introduce loss of detail which may affect the accuracy. This induces the search for anonymization algorithms that achieve the required level of anonymization while incurring a minimization of loss of information. Finding optimal anonymous datasets using generalization or suppression has been proved to be a NP hard problem. Therefore, some standard heuristic search Methods such as Genetic Algorithms (GAs), Particle Swam Optimization (PSO) and Ant Colony Optimization (ACO) can be used to find optimal datasets.