Multi cloud storage model leveraging distributed storage properties to securely store data in IoT de

The recent popularity of Internet-of-Things(IoT) significantly impacts cloud storage and data protection. The massive amounts of data generated from the IoT devices makes the cloud a perfect storage solution for such data. Cloud-based storage solutions for large scale data lay emphasis on properties like data privacy and data availability. Despite the multitude of advantages that the cloud has over on-premise storage, when it comes to access control and privacy of IoT data storage using a single cloud provider has major disadvantages. The storage provider has complete control over secure computations on the data which means they can use the data for their own purposes. Additionally, the user depends solely on the provider to access all the data where the single point of access has adverse impacts. Any failure on the provider's side could result in complete loss of data and data could easily be compromised if the storage provider is broken into. These disadvantages promote the need to apply distributed storage properties to secure IoT data. We propose a multi-cloud storage model where IoT data generates chunks that are shared among multiple cloud storage providers. No single provider has knowledge of the entire data which improves data privacy. As an added benefit, our model leverages the properties of secret-sharing allowing us to regenerate the data as long as a sufficient number of storage providers are functional. Since most IoT devices are small with minimum space for memory processing, we use a gateway device to perform all our computations. While the gateway device solves the minimum memory requirement criteria in IoT devices, gateway device management proves to be difficult. As future work we plan to look into ways to secure gateway devices so that data cannot be interceded during transfer. Eventually we plan to eliminate the need for gateway devices and use more efficient on-device memory like NVMs to perform computations at the device level.

Monday, May 21, 2018 at 12:30 PM


Material from the event

CRSS Contact:
Mukhopadhyay, Sinjoni

Last modified 15 May 2018