A Secure Bandwidth-Efficient Treatment for Dropout-Resistant Time-Series Data Aggregation

ePrint Report: A Secure Bandwidth-Efficient Treatment for Dropout-Resistant Time-Series Data Aggregation

Reyhaneh Rabaninejad, Alexandros Bakas, Eugene Frimpong, Antonis Michalas

Aggregate statistics derived from time-series data collected by individual users are extremely beneficial in diverse fields, such as e-health applications, IoT-based smart metering networks, and federated learning systems. Since user data are privacy-sensitive in many cases, the untrusted aggregator may only infer the aggregation without breaching individual privacy. To this aim, secure aggregation techniques have been extensively researched over the past …

aggregation aim applications bandwidth cases data eprint report federated learning health iot may networks privacy report series smart statistics systems untrusted user data