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How can data giants survive under the protection of increasingly serious privacy terms?

In April this year, Apple implemented new privacy terms on the iOS 14.5 operating system, saying that when installing software in the future, if the software collects the privacy of an iOS device user’s online activities by “crossing multiple company software and websites,” it must first obtain the user’s permission. This is undoubtedly good news for users troubled by privacy issues. However, at the same time, this policy is obviously a new challenge for the $100 billion advertising market.

The introduction of privacy policy requires some data-based enterprises to find solutions. In addition to expanding other operations, some enterprises also began to solve the problem of data utility by improving algorithms or adopting privacy protection technology. GoodData blockchain adopts a privacy protection solution combining blockchain technology and privacy computation. Privacy computation completes the availability and invisibility of data through machine learning, secure multi-party computation, homomorphic encryption, and other technologies to realize multi-party federated computation without data leaving the location; blockchain helps to improve node governance, use smart contracts to complete automatic benefit distribution and data sharing incentives, and data verification and traceability through consensus. This combination can greatly meet the increasingly serious information security specifications, higher data security, and personal privacy needs, as well as the new enterprise cooperation mode.

Next, we will briefly introduce the main concepts and functions of secure multi-party computation, machine learning, federated learning, differential privacy and other technologies used in the GoodData blockchain:

Secure Multi-Party Computation refers to the secure collaborative computing through the joint participation of multiple parties without a trusted third party. That is, in a distributed network, each participant holds its own secret input and wants to complete the calculation of a function together, though each participant can not get any input information of other participating entities except the calculation results.

Machine learning (ML), as a multi domain interdisciplinary subject. It involves probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in how computers simulate or realize human learning behavior, so as to obtain new knowledge or skills, reorganize the existing knowledge structure and continuously improve performance. It is the core of artificial intelligence and the fundamental way to make computers intelligent.

Federated learning is an emerging basic artificial intelligence technology. It was originally used to solve the problem of Android mobile phone end users updating models locally. Its design goal is to start efficient machine learning between multiple participants or multiple computing nodes on the premise of ensuring the information security during big data exchange, protecting the privacy of end equipment’ data and personal data privacy, and ensuring legal compliance.

Differential privacy is a data sharing method that can share only some statistical characteristics of the database without disclosing personal information. The intuitive idea behind differential privacy is: if the impact of randomly modifying a record in the database is small enough, the obtained statistical features can not be used to deduce the content of a single record. This feature can be used to protect privacy.

In today’s explosive big data era, the privacy security problem is always a noteworthy issue. However, data privacy protection must not be at the expense of data utilization. As the “crude oil” of science and technology in the new era, we have enough confidence to realize the protection of data privacy and the rational development and use of data through scientific and technological means. On the road of data value mining, GDF(Good Data Foundation) has always been unafraid of difficulties and devoted itself to study, so that the GoodData blockchain can truly give full play to data value sharing in the big data era.

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