This year, Facebook CEO Zuckerberg claimed to transform Facebook from a social media company to a Metaverse company within 5 years, and said that the company is setting up a product team for the development of Metaverse. Recently, Facebook announced that Andrew Bosworth, the Chief of AR / VR group , will take over as chief technology officer in 2022. Zuckerberg said in a statement: As our next CTO, Boz will continue leading Facebook Reality Labs and overseeing our work in augmented reality, virtual reality and more, and as part of this transition, a few other groups will join Boz’s team as well.

The rise of the Metaverse has brought AR / VR a new life. In the long run, the demand for digital asset trading will become more and more prominent in the process of the economic development of the Metaverse. NFT, as the underlying infrastructure, or will play an instrumental role, capitalizing all virtual objects in the Metaverse, and forming a safe and reliable economic environment through blockchain recording and trading.

The development of Metaverse has also accelerated the transformation of data assets. NFT helps data ownership return to the data owner itself. As a blockchain platform focusing on data value mining, GoodData capitalizes user data and constructs the right confirmation system of user data and future community for the first time. The GoodData blockchain allows sleep data to be recorded in the blockchain and confirmed through NFT, so that it can be traced and cannot be tampered with, so as to ensure that every user can obtain data rights fairly.
 
In the data right confirmation, GoodData considers the data security in the construction Metaverse in advance, and ensures the value mining and safe storage of data through privacy computation and decentralized storage, so as to protect users' privacy.
 
With the launch of GoodData blockchain, user data will make great contributions to the development of healthcare and other fields. Through NFT and GooD (GoodData native token), data rights will really return to the data owner itself.

The GoodData blockchain will build a value right confirmation system for user data and a future form of community order. The first step has been taken in the Metaverse of GoodData blockchain with sleep data as the core.  There will be more possibilities in the Metaverse  future. As a pioneer of data value mining, GoodData has made full preparations for the arrival of Metaverse.

Come join us! https://t.me/gooddatafound      #bitcoins  #Metaverse  #Blockchain  #NFTs #BlockchainGaming

In the process of data value release, GoodData realizes data privacy protection and secure sharing through the combination of various technologies. In the last article, we talked about the important role  that differential privacy plays in preventing data disclosure. Now, we will introduce a technology applied by GoodData to assist multiple participants in machine learning on the premise of ensuring data privacy and security: federated learning.

The concept of federated learning

Federated learning is a machine learning technology that can train algorithms between multiple distributed edge devices or servers with local data samples without exchanging data samples. Participants do not need to transfer data to the server, but instead to the local training model. It only needs to transfer parameters between the server and each node, which solves the problem of data privacy.

According to the different data distribution among multiple data owners, federated learning can be divided into three categories: horizontal federated learning, vertical federated learning, and federated transfer learning.

Horizontal federated learning

Horizontal federated learning refers to the joint learning of participants when there is more overlap of sample features but less overlap of users. For example, banks A and B in different regions have similar businesses, but different users. With the cooperation of a third party (such as GoodData), the system aligns the encrypted samples of the data of A and B, selects samples with the same characteristics but different users, and then jointly trains a machine learning model in GoodData. In this process, participants' data are trained in an encrypted environment. Data privacy protection is guaranteed.
Vertical federated learning

Vertically federated learning aims at joint learning among multi-party data owners with less sample feature overlap but more user overlap. For example, hospital A and bank B in the same region have data from users in the region. Due to different businesses, the sample special diagnosis is also different. Through vertical federated learning, both A and B can jointly improve the model effect on the premise of data protection, and will not lose their original data.

Federated transfer learning

Federated transfer learning is applicable where there is little overlap of characteristics and samples among participants, such as the combination of hospital A and bank B in different regions. Essentially, both data owners use the similarity between data to apply the model learned in the source domain to the target domain. This learning process is similar to a person who can sketch to learn to draw watercolor.

Application of federated learning

The contribution of federated learning to data security and sharing makes it widely used in various fields. Take the medical industry for example.

The problem of data "information silos" in the medical and health field is a major obstacle to the development of the industry. There is no interconnection and unified standard for medical data between different hospitals in different regions. Federated learning allows medical institutions to update model parameters only by transmitting encrypted information through protocols without uploading their medical data to the server or exchanging data samples, so as to realize the training results of using data without exposing data privacy. In addition, federated learning can solve key problems such as data rights confirmation, privacy protection and access to heterogeneous data, which provides a strong support for the Metaverse constructed by data in the future. It is also an indispensable part of the GoodData blockchain to realize data monetization.
Summary of GoodData’s official account
website： https://goodata.io/
WeChat subscription account：Good Data Foundation
White paper：https://goodata.io/whitepaper.pdf
Medium：https://medium.com/gooddatafound
Twitter：https://twitter.com/gooddatafound
Telegram ：https://t.me/gooddatafound
Discord：https://discord.gg/v9WayuMpHs
Reddit：https://www.reddit.com/r/gooddata/
Weibo：https://weibo.com/7579699020

As the most important asset in the 21st century, data has increasingly attracted the attention of our whole society. Data sharing is an extremely important way to release data value. However, there seems to be a dilemma between data sharing and privacy protection. Balancing data sharing and privacy protection is a difficult problem. Fortunately, with continuous developments in computer technology and cryptography, this demand can be realized through the integration of a series of technologies. In this article, we will share one of the most common privacy computation technologies - differential privacy.

Concept of differential privacy

With regard to the concept of differential privacy, Wikipedia explains that it is a means of data sharing that can share only some statistical features and can describe the database without disclosing personal information. The intuitive idea behind it is this: if the impact of randomly modifying a record in the database is small enough, the obtained statistical characteristics cannot be used to deduce the content of a single record. This feature can be used to protect privacy.

The core technique behind differential privacy is to make the query result become a random variable by adding noise to the database. The less data the query requests, the more noise will be added to ensure the same degree of privacy.

Application of differential privacy

Let’s say you have a database of academic qualifications. In this database, there are 10 people with primary school education, 20 people with middle school education and 30 people with university education. The number of people with various degrees are searchable. Now another sample is registered in the database. After searching again, it is found that 31 people have college degrees. Thus, we can conclude that the educational background of the newly entered sample is a university degree. In this example, we find that even if we can't query the information of each sample, the statistical database may disclose the information of specific samples. Differential privacy is designed to solve the problem of data leakage caused by the above situation.

Let’s apply differential privacy to the example above. In this education database, by adding Laplace noise, the number of college degrees users queried about is 29.5. We  then add a sample of college degrees, and the result is still about 29.5. The results of the two queries are very similar, so the newly entered sample information will be hidden.

In the GoodData blockchain, differential privacy is applied in the GoodData machine learning (ML) SDK to protect the data privacy of data owners. The original data shared by the data owner is encrypted and protected by differential privacy, which ensures that the data owner is the only node that has the original data.

The above only explains and gives examples of differential privacy from a non-technical perspective to help ordinary users better understand the technical principle behind privacy computation. Privacy computation is complex and rigorous. To realize data sharing and privacy protection, we also need the support and cooperation of many other technologies, which will be introduced in the following articles. You can subscribe here.