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Matrix AI Network: Matrix 2.0 platform to protect data privacy and 186 Verification Masternodes to be online for 2500 minutes

Biweekly update 25th July — 8th August

During the last two weeks, Matrix AI Network illustrated the average level of social activity. Data is fundamental to the AI industry as it pertains to AI model training. However, as data transparency becomes an increasingly important topic in the big data era, the issue of how to avoid data leakage comes into focus. This is an issue that should be of particular concern to the blockchain industry. Matrix 2.0 platform offers distributed storage solutions during data upload, as well as by using federal learning, homomorphic encryption, and secure multi-party computation during data computation. Plus Matrix AI Network published the report that highlights the real impact of the numerous network adjustments and upgrades. This update contains data for two months. Between June and July 2019, a total of 186 Verification Masternodes were online for at least 2500 minutes. Stay tuned!


Github metrics
Developer activity (from

This article introduces how the Matrix AI Network protects data privacy by underpinning its Matrix 2.0 platform with distributed storage solutions during data upload, as well as by using federal learning, homomorphic encryption, and secure multi-party computation during data computation.

Secure Upload

As data sharing and collaboration come to prevalence in today’s big data era, traditional blockchain solutions designed to openly store data cannot meet the growing needs of data protection. As a result, the blockchain industry will focus on data protection while supporting various forms of data attribution to allow a distinction between the right to access, use and own data.

The Matrix AI Network is building a blockchain-based AI economy where recording data directly to an encrypted distributed ledger ensures exclusive attribution of data ownership rights and prevents data from being furtively reproduced. Such a distributed storage system can ensure data privacy while bridging data islands to create fertile new opportunities for big data to evolve; including collaboration and profit split on data generation, modeling, applications.

Secure Computation

To efficiently protect data privacy, Matrix AI Network has introduced the following three major technologies:

Federated learning

Federated learning is a distributed training method for machine learning (ML). Each device only processes a portion of the ML training task, and the training results are integrated later. With this technology, no individual device can have access to complete data so that data privacy can be properly protected.

Homomorphic encryption

Homomorphic encryption is a form of encryption that allows computation on ciphertexts, generating an encrypted result which, when decrypted, matches the result of the operations as if they had been performed on the plaintext. With this technology, users asking for computing power will no longer open plaintext to cloud server, which can efficiently avoid data leakage.

Secure multi-party computation (SMC)

SMC is a subfield of cryptography with the goal of creating methods for parties to jointly compute a function over their inputs while keeping those inputs private and preserving the independence and computational accuracy. Such a framework has been widely used in fields involved with sensitive information such as customer behavior, identification, credit inquiry and so on. On the Matrix 2.0 AI-powered blockchain platform, SMC ensures data scientists jointly process separately-stored data and except computational results, inputs cannot open to all nodes.

As high-quality data can better train AI models so as to efficiently advance the development of AI technology, the Matrix AI Network endeavors to protect data privacy.

Social encounters

An updated Medium article ‘An Introduction to the Matrix AI Network’. This current piece gives a more detailed analysis of the Matrix AI Network.

See also:

“Simple Matrix” is a series of tutorial videos outlining a variety of Matrix AI Network functions. This set of Simple Matrix videos will focus on the Matrix Web Wallet. let’s look at how to transfer MAN tokens using the Matrix Web Wallet.


The number of transactions (information from

Since April, the Matrix AI Network has been publishing regular Verification Masternode participation incentive statistics. The Matrix AI Network’s participation incentives will continue to adapt, change and normalize over time. As this update contains data for two months, there is a less obvious statistical fluctuation. These reports help highlight the real impact of the numerous network adjustments and upgrades.


· One data sample is used: June 1st, 2019 to July 30th, 2019

· Data only considers block rewards. Stake rewards and other incentives such as airdrops are not included in this calculation

· Only Verification Masternodes that have been online for at least 2500 total minutes and never receive incentives are included to ensure greater accuracy and scalability of results

· Punished Verification Masternodes that have been forced to forfeit selection opportunities are included in the data. The Matrix team will consider excluding punished Masternodes in future reports

Verification Masternode Annual ROI — June 1st, 2019 to July 30th, 2019

Between June 1st and July 30th, 2019, a total of 186 Verification Masternodes were online for at least 2500 minutes. The number of recorded Verification Masternodes per stake level is as follows:

The deviation between Stake Levels

There are several factors contributing to this including the relatively low minimum uptime requirement for inclusion in this data set (2500 minutes) and the unequal number of nodes at each stake level. Raising the sample size to be online for over 5000 minutes or 10,000 minutes illustrates this phenomenon. Additionally, if there are only a small handful of nodes in a given stake level, a single malfunctioning node can skew the data.


What’s next?

  • FPGA mining will be available by the end of 2019.
  • Up to 10 DAPPS by the end of 2019

Partnerships and team members

No updates


No updates

Social media metrics

Social media activity
Social media dynamics
Social media dynamics

The chart above illustrates a slight decline in the number of Telegram followers. In general, Matrix AI Network experiences the average level of social activity.

  • Twitter (Matrix AI Network) — official announcement channel (100–400 likes per publication).
  • Facebook — official announcement channel — duplicates news from Twitter (10–20 likes per publication).
  • Reddit — news about projects and blockchain, links to interviews, podcasts, upcoming events.

The graph above shows the dynamics of changes in the number of Matrix AI Network Reddit subscribers, Twitter followers, and Facebook likes. The information is taken from

This is not financial advice.

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