What is privacy computing technology?
Driven by the wave of digitization, technological innovation, scenario application and management services in the data field have become an important driving force for the development of digital transformation in various industries.
Security vs. development
Security and development have always been two important themes in the field of data management. Both are contradictory and opposing, constraining each other; but also in the pursuit of balance under the continuous technological innovation, to maximize the realization of the value of the data.
Constraints
"Data" as a special market resource and production factor, its own characteristics determine that only in a wider range of social *** enjoyment in order to play its real resources.
Artificial Intelligence, Big Data, Cloud Computing and other technologies are rapidly expanding, Continuously improving computing power and optimizing algorithms, we will discover the laws of the matter through different dimensions, different areas of big data, and use the laws to explain the past and predict the future.
The important premise of continuous optimization and enhancement of intelligent algorithms is to conduct data training through massive and diversified big data resources, objectively, there is a strong demand for the use of data **** enjoyment, which is related to the blockchain, which has the characteristics of "information data **** enjoyment and transparency". " Blockchain Technology is a technology that has been developed and applied rapidly in recent years.
But it's important to note that, while the exchange of data *** enjoyment enhances the value of the data itself, it also inevitably infringes on the security of the data owner's data privacy, and the data *** enjoyment of mining is facing compliance regulation, and the application of data technology development is stuck in a bottleneck.
Balanced development
"Finding a balance between the contradictions" is an important issue in the field of technological innovation and application of data.
The rapid change of the objective market environment has also formed a strong driving force for the balanced development of "data flow" and "data security".
At the end of 2019, a sudden new coronavirus epidemic spread around the world, killing a large number of people and dealing a heavy blow to the economic development of various countries. Under the objective situation of epidemic prevention and control, digital transformation and development has become an important strategic measure for countries to restore economic order and establish a new international competitive advantage. In this context, data as a new production factor, with the function of value continue to improve, the technology application continues to expand, data "circulation use" and "security" is also increasingly by the industry development and government supervision of the attention.
These are the first steps in the process.
The innovative application of data technology, on the one hand, poses a new challenge to data security, and, on the other hand, provides a corresponding answer in the form of technological innovation - "blockchain + privacy computing".
Blockchain+Privacy Computing
Trust Mechanisms and Privacy Protection in the Data Era
Blockchain technology is a technology that collectively maintains a reliable database by means of a decentralized, high-trust program.
Blockchain technology is a technology solution that collectively maintains a reliable database through a decentralized, high-trust approach. Due to its advantages of "decentralization", "distributed data storage", "traceability", "tamper-proof", "openness and transparency" and other characteristics, blockchain technology can effectively solve the problem of data authenticity, security and openness in the field of data, and prevent and avoid all kinds of data management problems such as data forgery, tampering, and loss by building a trustworthy data management environment, promoting efficient **** enjoyment of data. It promotes the efficient ****enjoyment and application of data.
As mentioned above, blockchain technology is characterized by "information and data *** enjoyment and transparency", but no one wants their data to be completely open and transparent, whether from the perspective of market competition or personal information security. Therefore, privacy compliance has become an important "red line" in the field of data management, protecting the privacy and security of data owners on one hand, and affecting the efficiency and development of data flows*** on the other.
Is there a technology that can ensure the efficient flow of information and data without crossing the red line of privacy compliance?
If "blockchain" technology establishes trust in the data age, then "privacy computing" establishes a secure privacy defense for data owners in the midst of a flood of data ****.
Privacy Computing, a computational theory and methodology for the full lifecycle protection of private information, is a computational model and axiomatic system for the complexity of privacy metrics, privacy leakage costs, privacy protection, and privacy analytics in the context of separation of ownership, management, and use of private information. Axiomatic system. Simply put, privacy computing is a technical approach to protect privacy from the generation, collection, preservation, analysis, utilization, and destruction of data.
Like blockchain technology, privacy computing does not refer to a specific technology, but is a comprehensive technology application that integrates cryptography, data science, economics, artificial intelligence, computer hardware, software engineering, and other disciplines. Privacy computing includes a series of information technologies, such as secure multi-party computing (MPC) technology proposed earlier by the industry, Trusted Execution Environment (TEE) technology featuring isolation and protection of hardware technology, cryptography and distributed computing based on the implementation of Federated Learning (FL) techniques for multi-party collaborative machine learning, as well as ancillary techniques such as homomorphic encryption, zero-knowledge proofs, and differential privacy, all fall under the category of privacy computing.
Secure multiparty computation (MPC), is a technique and system for securely computing an agreed-upon function without the participants ***sharing their respective data and without a trusted third party. Through secure algorithms and protocols, participants encrypt or transform data in plaintext before making it available to other parties, and no participant has access to the other parties' data in plaintext, thus ensuring the security of each party's data.
Trusted Execution Environment (TEE) is a secure area of the CPU that is separate from the operating system and is not affected by the operating system. Data stored and computed in this secure area is not affected by the operating system, and is confidential and untamperable.
Federated Learning (FL), is a method of machine learning where multiple parties collaborate on training without ****ing on local data. Federated learning technology supports data not going out of the domain, but rather allowing the algorithmic model to move, and optimizing the algorithmic model by training on the data and then optimizing the algorithmic model.
The goal of privacy computing is to make the data available and invisible during the flow, i.e., to output only the results of the data but not the data itself.
Data Ready is a video ready to be used as a stand-alone application for Windows Media Player.
For example, in the field of medical data, various types of medical data, privacy requirements are higher, the amount of data is larger, usually only stored in the information system of the local organization, it is difficult to realize the efficient circulation of medical data, **** enjoyment and use, and can not be used for various types of pathology in the medical field.
These are the first time I've ever seen a computer that could be used as a computer for a computer, and the second time I've seen a computer that could be used for a computer, I've never seen one.
However, if we can collaborate with privacy computing technology to ensure that data is "available but not visible", we can achieve efficient circulation of medical data across different regions and different healthcare institutions, and continue to optimize various algorithmic models in the medical industry, which will help us achieve precision medicine, telemedicine, smart medicine, and other medical technology service innovations.
Under the wave of digital development, "data" as a new and important market resource and production factor, its rapid development and management applications are increasingly being emphasized by the state, and constantly empowering the development of various industries. In the meantime, the privacy and security issues in the data space have also put data management applications in a difficult position. It is foreseeable that the combination of blockchain technology and privacy computing technology will be an important attempt to explore the field of data management, which will have a significant impact on the development of the data field.