New privacy preserving clustering methods for secure multiparty computation
نویسندگان
چکیده
منابع مشابه
New privacy preserving clustering methods for secure multiparty computation
Many researches on privacy preserving data mining have been done. Privacy preserving data mining can be achieved in various ways by use of randomization techniques, cryptographic algorithms, anonymization methods, etc. Further, in order to increase the security of data mining, secure multiparty computation (SMC) has been introduced. Most of works in SMC are developed on applying the model of SM...
متن کاملNew Privacy Preserving Back Propagation Learning for Secure Multiparty Computation
Many studies have been done with the security of cloud computing. Data encryption is one of typical approaches. However, complex computing requirement for encrypted data needs a great deal of time and effort for the system in this case. Therefore, another studies on secure sharing and computing methods are made to avoid secure risks being abused or leaked and to reduce computing cost. The secur...
متن کاملPrivacy Preserving Fuzzy Modeling for Secure Multiparty Computation
Many studies on privacy preserving of machine learning and data mining have been done in various methods by use of randomization techniques, cryptographic algorithms, anonymization methods, etc. Data encryption is one of typical approaches. However, its system requires both encryption and decryption for requests of client or user, so its complexity of computation is very high. Therefore, studie...
متن کاملSecure Multiparty Computation for Privacy-Preserving Data Mining
In this paper, we survey the basic paradigms and notions of secure multiparty computation and discuss their relevance to the field of privacy-preserving data mining. In addition to reviewing definitions and constructions for secure multiparty computation, we discuss the issue of efficiency and demonstrate the difficulties involved in constructing highly efficient protocols. We also present comm...
متن کاملSecure Multiparty Computation for Privacy Preserving Data Mining
Acknowledgments This thesis is the result of my internship at Erasmus University Rotterdam, as part of the the EU-FP7 project CASSANDRA. I would like to thank professor Hennie Daniels for giving such an opportunity to perform an interesting and challenging master's thesis project. I am very grateful to my supervisor Berry Schoenmakers at Eindhoven University of Technology , for the guidance, qu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Artificial Intelligence Research
سال: 2016
ISSN: 1927-6982,1927-6974
DOI: 10.5430/air.v6n1p27