Association rules based secured dataset using Dynamic Pivot Tuple (DPT) Technique
The main ingredients in our protocol are two novel secure multi-party algorithms. One that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another (Han et al.,2001). Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is used. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout and returning a set of numbers instead of one number per row. This new class of functions is called (DPT) Dynamic Pivot Tuple. Horizontal aggregations build data sets with a horizontal demoralized layout (e.g., point-dimension, observation variable, instance-feature), which is the standard layout required by most of the data mining algorithms. We proposed three fundamental methods to evaluate horizontal aggregations: 1.CASE: Exploiting the programming CASE construct; 2.SPJ: Based on standard relational algebra operators (SPJ queries) and 3.PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables were compared with the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, whereas the SPJ method does not.
SQL data, aggregation SPJ, Pivot, Case