There is a compelling need for data privacy and security in the coming Internet of Things (IoT). With the Internet of Everything (IoE) there is the need for data sharing between enterprises to gain the most benefit. To address these needs, we developed a Platform-as-a-Service (PaaS) data sharing platform, the Dataparency Platform™.
Using privacy rules defined by the controlling entity, data is accessed through rule controlled views. Access rights are determined by the endpoint, its role and whether it is authorized by the entity.
We handle your big data problem, the four V's; Volume, Variety, Velocity and most importantly Veracity.
With Dataparency Embedded™ in your NodeJS gateway, you have the ability to build smart gateways that comply with the WWW Web-of-Things (WoT) Gateway Integration Specification. Process and manage data locally and expose/send only necessary data over the Net reducing network loads, costs, and attack surface. For example, providing a gateway for controlling all of a hospitals devices, providing effectively, a software firewall protecting those devices and their data from external hacking.
Transparency with Privacy℠
Smartness in your IoT gateway means being able to conform/transform the variety of your device interfaces to a standard that can then be exposed to applications. Your gateway can become the "translator" that talks to your devices in their own language. It acts as the First Receiver to your IoT data, managing the sharing and distribution of data to your partners endpoints. Commonality in your IoT data allows development of functionalities not possible otherwise.
The IoT will require storage of massive amounts of device time series data to be useful. This can be accomplished with Dataparency by using it's database embedded within the node.js server of your IoT gateway. With it's small memory footprint, you can store millions or billions of data points to be securely exposed on the Net for analysis and monitoring of devices.
Dataparency's embedded platform allows you to place computing power close to the data sources for aggregation and analytics purposes. Doing so reduces the network load, allowing full-scale IoT to be implemented without the build-out of network infrastructure otherwise necessary to handle the terabytes of data that sensors and devices produce. It also allows real-time event publishing to be controlled at the endpoint where conditions are best handled.
We provide a Hyperallel™ (hyper parallel) distributed computing model that offers scaling not possible in other systems.
Again, we handle your big data problem, the four V's; Volume, Variety, Velocity and most importantly Veracity.