This paper presents a toolkit of privacy-preserving distributed data mining techniques to address the challenges of privacy in data mining. The authors propose that privacy-preserving data mining requires a set of components that can be combined to solve specific privacy-preserving data mining problems. They describe several privacy-preserving computations, including secure sum, secure set union, secure size of set intersection, and scalar product, which can be used to solve privacy-preserving distributed data mining problems. The paper discusses the importance of secure multiparty computation (SMC) in privacy-preserving data mining, where multiple parties can collaborate to compute a function without revealing their individual data. The authors also highlight the challenges of ensuring privacy in data mining, such as the need to avoid disclosing individual transactions while still being able to compute global statistics like support and confidence for association rules. The paper presents several applications of these techniques, including association rule mining in horizontally and vertically partitioned data, and EM clustering for secure clustering. The authors emphasize the need for a toolkit of privacy-preserving computations that can be assembled to solve specific real-world problems, rather than developing solutions for individual problems. They also discuss the challenges of ensuring privacy in iterative techniques and the need for further research in this area. The paper concludes with a call for continued research and development in privacy-preserving data mining.This paper presents a toolkit of privacy-preserving distributed data mining techniques to address the challenges of privacy in data mining. The authors propose that privacy-preserving data mining requires a set of components that can be combined to solve specific privacy-preserving data mining problems. They describe several privacy-preserving computations, including secure sum, secure set union, secure size of set intersection, and scalar product, which can be used to solve privacy-preserving distributed data mining problems. The paper discusses the importance of secure multiparty computation (SMC) in privacy-preserving data mining, where multiple parties can collaborate to compute a function without revealing their individual data. The authors also highlight the challenges of ensuring privacy in data mining, such as the need to avoid disclosing individual transactions while still being able to compute global statistics like support and confidence for association rules. The paper presents several applications of these techniques, including association rule mining in horizontally and vertically partitioned data, and EM clustering for secure clustering. The authors emphasize the need for a toolkit of privacy-preserving computations that can be assembled to solve specific real-world problems, rather than developing solutions for individual problems. They also discuss the challenges of ensuring privacy in iterative techniques and the need for further research in this area. The paper concludes with a call for continued research and development in privacy-preserving data mining.