PDF-DS: Privacy-Preserving Data Filtering for Distributed Data Streams in Cloud
Many real-world applications, from traditional wireless sensor networks to today's Internet of Thing (loT), generate a large amount of data streams distributively. To meet challenges of handling distributed data streams, deploying data streams management systems on public clouds is a prevalent choice. However, as data streams may contain sensitive information, appropriate privacy protection mechanisms must be in place when sending these data to the cloud. Aiming to utilize data outsourced to the cloud without disclosing their privacy, a number of functional encryption schemes have been proposed. Nevertheless, these existing schemes either only consider centralized data source or require pre-defined indexes.
This paper proposes a privacy-preserving filtering scheme for distributed data streams outsourced to the public cloud. Our scheme allows cloud servers to filter out corresponding data streams directly over encrypted data. Our scheme enables each data source to encrypt its data independently, and thus the compromise of one data source will not reveal the privacy of others. Thorough analysis and experimental evaluation are carried out to demonstrate the security, effectiveness, and efficiency of our scheme.
Tian, Yifan; Yuan, Jiawei; and Hou, Yantian. (2019). "PDF-DS: Privacy-Preserving Data Filtering for Distributed Data Streams in Cloud". 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 1217-1224. https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00204