The dream of future medicine is to understand the link between DNA and disease, and to use this as a basis for developing personalized treatment options for patients. However, scientists realize that such "personalized" or "precise" medicine has a problem: how to use the powerful computing power of the cloud to find out the meaningful relationship between genes and diseases while ensuring the safety of genetic data and medical records. Association.

Currently, an emerging data encryption technology may solve this problem.

The University of California, San Diego (UCSD) is exploring how to combine cryptographic techniques for genetic data analysis. They use a method called "homomorphic encryption" in a smaller data set that can be found in 10 minutes. Genetic variants associated with disease. Although in practice, it takes hours for a computer to find disease-related genetic variants from a genome-wide data set consisting of hundreds of thousands of DNA fragments, cryptographic experts are still worth encouraging.

Homomorphic encryption is an encryption form that allows people to perform specific algebraic operations on ciphertext to obtain the result of encryption. The result of decrypting it is the same as that for plaintext. In other words, this technology allows people to perform operations such as retrieving and comparing in encrypted data to get the correct results without decrypting the data throughout the process. The significance is to fundamentally solve the confidentiality problem when delegating data and its operations to third parties, such as for various cloud computing applications.

This has always been an important topic in the field of cryptography. In the past, people only found some ways to achieve this kind of operation. In September 2009, Craig Gentry's paper mathematically proposed a feasible method of "full homomorphic encryption", that is, any data that can be encrypted without decryption can be in plaintext. The calculations carried out made this technology a decisive breakthrough. People are studying more perfect practical technologies on this basis, which is of great value to the information technology industry.

- from Wikipedia

Xiaoqian Jiang, a computer scientist at UCSD, said, "This is a foreseeable result, but the challenge remains."

Doctors and researchers believe that understanding the relationship between genes and diseases requires collecting data from millions of people, both genetically and physically. Some planning projects have been launched, such as the precision medicine project initiated by US President Barack Obama and the 100,000 genome project in the UK. Such a complex task may require the processing power of the Internet cloud host, but in the past few years, network security vulnerabilities have exposed the huge hidden danger of storing a large amount of sensitive data in the cloud. The National Institutes of Health's Genotype and Phenotype Database (dbGaP) has a directory of medical and genetic data, and its managers are very concerned about security issues. They prohibit database users from storing data on networked computers.

Homomorphic encryption solves this concern, allowing researchers to store data in the cloud using mathematical encryption. The technology encrypts the data on the local computer and then uploads the encrypted data to the cloud. The calculation of encrypted data can also be performed in the cloud, and the result of the calculation is encrypted and then transmitted to the local computer. Even if someone steals encrypted data in the process, the hidden content contained in the encrypted data is still safe.

According to Lucila Ohno-Machado, a computer scientist at UCSD, "If you can ensure that this technology works, it is essential to solve the problem of computing and storing massive amounts of data while protecting personal privacy. This can enhance our confidence. "

In 1978, homomorphic data encryption technology was first proposed. Unlike other methods, this technology can process encrypted data in the cloud. In essence, the cloud never really "sees" data processing. Another difference is that the technique can also give the same data processing results in an unencrypted state.

Until 2009, cryptographer Craig Gentry of the IBM Watson Research Center demonstrated the possibility of any form of computation for homomorphic encrypted data. At this time, this concept is still largely at the theoretical level. This method converts each data point into a piece of encrypted information or ciphertext, but the amount of encrypted data is larger and more complex than the original data. Each unencrypted byte is encrypted into several megabytes of data, which is equivalent to the size of a digital photo.

Although this is a breakthrough, but such a large difference in capacity before and after encryption also makes this technology can not be applied, know the hardware cost of data storage is very high.

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