TY - JOUR AU - Yigzaw, Kassaye Yitbarek AU - Michalas, Antonis AU - Bellika, Johan Gustav PY - 2017 DA - 2017/01/03 TI - Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation JO - BMC Medical Informatics and Decision Making SP - 1 VL - 17 IS - 1 AB - Techniques have been developed to compute statistics on distributed datasets without revealing private information except the statistical results. However, duplicate records in a distributed dataset may lead to incorrect statistical results. Therefore, to increase the accuracy of the statistical analysis of a distributed dataset, secure deduplication is an important preprocessing step. SN - 1472-6947 UR - https://doi.org/10.1186/s12911-016-0389-x DO - 10.1186/s12911-016-0389-x ID - Yigzaw2017 ER -