A Novel Framework using Apache Spark for Privacy Preservation of Healthcare Big Data

The privacy of sensitive data in healthcare big data should be protected so that it does not intrude the privacy of specific patient or transactions of health care organizations. Privacy protection is a major concern in big data, therefore, demanding resilient approaches for the safeguarding of customer privacy. The proposed novel framework spark employs K-anonymization and L-diversity to mask the personal sensitive information and Apache Spark to handle health care big data in a faster and effective way. Thus proposed approach make sure that shared data will not disclose the original data and segregation of sensitive data happens before it is a move to HDFS.