"Internet plus" big data era: is your personal privacy data secure?
Smart tourism, smart health, smart transportation, online banking, online shopping and so on, bring great convenience to people's life, work and travel. The combination of big data, cloud computing, Internet and other new technologies is rapidly and will increasingly profoundly change people's production and life style. When people are more and more aware of the advent of the era of global big data, our life and work are more convenient. At the same time, there are some confusion, even doubts and worries. This is because of the emergence of massive data collection, interoperability, sharing and related industries, accompanied by data insecurity and personal privacy difficult to protect.
Data sharing, co integration and co governance have become the inevitable trend of social development. However, privacy protection and data openness are inseparable and equally important. Data opening has become an inevitable choice for the development of the times. In the era of big data, with the rapid development of mobile Internet, all kinds of network entrances such as mobile phones and ubiquitous sensors will collect, store, use and share personal data whenever and wherever they are. Most of these things happen when users can't control and know.
Perhaps from the moment of the emergence of the Internet, the struggle between data protection and data intrusion is doomed to become an inevitable fate. Network security has become a national strategic force from the military protection during the cold war. No matter the government, business institutions or individuals are suffering from the loss of information leakage, no one can stay out of the matter.
Blockchain is an important technology to promote data security and privacy protection 。 Privacy computing, according to the definition of China information and Communication Research Institute, refers to the information technology that can analyze and calculate the data and verify the calculation results on the premise of ensuring that the data provider does not disclose sensitive data. In a broad sense, it refers to the computing system and technology for privacy protection, covering the whole process of data generation, storage, calculation, application, destruction and other information processes. The desired effect is to make the data "available and invisible" in all aspects. On the premise of ensuring data security, data can be freely circulated or shared to eliminate the problem of data island, so as to release greater value of data, improve production efficiency, and promote industrial innovation.
Crypto battle 2.0 will bring new hope for data security protection.
1) Multi party secure computing (MPC) technology based on cryptography. Through special encryption algorithms and protocols such as secret sharing, forgetting transmission, obfuscation circuit or homomorphic encryption, direct computation on encrypted data is supported. In theory, multi-party secure computing technology can achieve any computing "function" without considering the cost of "ideal", and achieve relatively high security. However, due to the rapid increase of data traffic, the loss of computing efficiency and the need for high computing power and other factors, the production of MPC technology has some limitations, and the relevant technical solutions are actively exploring.
2) Secure sandbox computing (TEE) technology based on trusted hardware. The core idea is to build a hardware security area, in which data is only calculated. The trusted execution environment tee is used to prevent the operating system from viewing the content of the application execution environment maliciously; The security sandbox is used to prevent malicious applications from controlling the operating system through special calls.
3) Federated learning technology based on artificial intelligence. In the horizontal dimension, each participant computes his own sample in the local training and only shares the gradient of model training; In the vertical dimension, each participant trains their own "vector mapping" and jointly trains the upper model. The integration of the two dimensions enables multiple data owners who do not trust each other to jointly conduct model training without sharing data.
4) Differential privacy protects the privacy leakage caused by a small change in the data source.
It is a revolutionary battle to subvert the past privacy, a free enjoyment of the future economic security ecology, and an unlimited business empire. It pays tribute to all the brave people in the battle of encrypted business sea.