The IoT group that I’ve been working with for the past few years has been absorbed into the OEM group which is carrying on an expanded function. This blog post from Steve Todd, Dell Technologies Fellow, details the development of data confidence work that has been contributed to the open source Linux Foundation to seed Project Alvarium.

Following is a quick summary. Go to the blog for additional information about trusted data work.

A team of Dell Technologies specialists finished building the first-ever Data Confidence Fabric (DCF for short). The prototype code will be contributed to the Linux Foundation to seed Project Alvarium.

For several years, the CTO of the Dell Technologies Edge and IoT business unit has been touting a vision of data monetization. However, it’s hard to monetize untrusted Edge and IoT data. As he likes to say, “It’s midnight. Do you know where your data has been?” 

Enterprise storage systems have delivered trusted data to applications for a long time. We started our initial investigation wondering if these same trust principles could be applied to Edge and IoT ecosystems. Recent developments in data valuationdistributed ledgers, and data marketplaces facilitated everything coming together.

Five Levels of Trust

We started with the EdgeX Foundry chair of the Core Working Group, Trevor Conn. Trevor wrote the first-ever Data Confidence Fabric software using Go Lang, the same programming language EdgeX is written in. His Data Confidence Fabric software registered with EdgeX as a client and began processing simulated device data. The initial confidence score for this data was “0” (no trust was inserted). 

Dell Technologies then hired three computer science interns from Texas A&M to deploy EdgeX and the Data Confidence Fabric software on a Dell Gateway 3000 with a Trusted Platform Module (TPM) chip.

EdgeX was then adjusted to support N-S-E-W authentication by using VMware’s open-source Lightwave technology.

Dell Boomi software was invoked by the Data Confidence Fabric software to gather provenance and appended this metadata to the sensor reading.

The Data Confidence Fabric software then stored the data locally using IPFS (an immutable, open-source storage system). This fourth level of trust insertion gives an application confidence that the data/provenance has not been tampered with. It also has the additional benefit of enabling analytics to access data closer to the source.

The Data Confidence Fabric software then registered the data into VMware’s blockchain (based on the open-sourceProject Concord consensus algorithm). 

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