$GTCH (GLOBE NEWSWIRE) -- GBT Technologies Inc. (OTC PINK: GTCH) ("GBT”, or the “Company”) is enhancing its qTerm device cybersecurity technology in order to ensure robust privacy and sensitive data protection for its potential users. Due to the rise of sensitive data breach cases in the past few years, GBT decided to add another layer of data protection, developing breakthrough techniques to prevent potential data theft. The enhancements will be performed within the device’s AI computer programs to increase data security for its machine learning and computing environments. GBT's qTerm, a human vitals intelligence device is targeted to measure human vitals with a touch of a finger, and includes AI technology for personal health monitoring. The device is accompanied by a smartphone app and a synchronized widget web application to keep a history and provide health related analytics.
GBT will be implementing Homomorphic Encryption (HE) techniques within its AI environment to enable encrypted data processing without decrypting it first. The qTerm algorithms send data back and forth over encrypted channels. The AI needs to perform computations and analysis and typically the system decrypts the information first, working on it, and re-encrypting it again before sharing it. This creates a potential security risk. HE technology enables robust data protection since the processing is always done with the encrypted data. HE techniques were known to have one major disadvantage, which is a very long processing time compared to decrypted data processing runtime. GBT developed new algorithms that operate with much higher performance enabling fast computations using HE methods. The company plans to implement HE technology within qTerm’s Machine Learning components, mobile and web interface computing environments.
“Our AI engine includes multiple modules that shares digital assets over internal channels. The machine learning sub-systems may communicate sensitive information, for example, sharing users medical/personal information between the data storage module and the data training module. In order to secure data, there are two major operations that are done, encryption and decryption. The encryption typically happens where the sensitive data is first captured, for example after qTerm device recording a user’s vitals. The data is then sent to the AI system for processing on the main data center. At the data center the data needs to be decrypted to work on, and then re-encrypted after completion. The decrypting phase introduces potential data breach risk. Using the HF technique eliminates the need for decryption, and all operations can be performed on the encrypted data which eliminates the major security risk. We plan to develop several types of algorithms in this domain that will use cryptography and mathematical methods to operate directly on encrypted data. In this way we’ll significantly enhance the data’s privacy and security. One of qTerm’s main purposes is to perform as a telemedicine device which communicates with its AI data center. User’s vital information will be sent via a web widget and HE based technology will ensure a high level of data security. AI systems require robust security mechanisms by their nature and by using HE we are preserving data privacy starting at the source. Particularly, with a telemedicine type device, like qTerm, the data will be encrypted and outsourced to its data center environment for processing, all while encrypted. In the past few years there is a constant growing concern about data privacy and security, and implementing new techniques and methods in this domain will ensure highly secured AI operation and computation. We believe that this is especially important for the qTerm’s device as it collects, processes and records sensitive personal and medical information,” said Danny Rittman the Company’s CTO.
GBT will be implementing Homomorphic Encryption (HE) techniques within its AI environment to enable encrypted data processing without decrypting it first. The qTerm algorithms send data back and forth over encrypted channels. The AI needs to perform computations and analysis and typically the system decrypts the information first, working on it, and re-encrypting it again before sharing it. This creates a potential security risk. HE technology enables robust data protection since the processing is always done with the encrypted data. HE techniques were known to have one major disadvantage, which is a very long processing time compared to decrypted data processing runtime. GBT developed new algorithms that operate with much higher performance enabling fast computations using HE methods. The company plans to implement HE technology within qTerm’s Machine Learning components, mobile and web interface computing environments.
“Our AI engine includes multiple modules that shares digital assets over internal channels. The machine learning sub-systems may communicate sensitive information, for example, sharing users medical/personal information between the data storage module and the data training module. In order to secure data, there are two major operations that are done, encryption and decryption. The encryption typically happens where the sensitive data is first captured, for example after qTerm device recording a user’s vitals. The data is then sent to the AI system for processing on the main data center. At the data center the data needs to be decrypted to work on, and then re-encrypted after completion. The decrypting phase introduces potential data breach risk. Using the HF technique eliminates the need for decryption, and all operations can be performed on the encrypted data which eliminates the major security risk. We plan to develop several types of algorithms in this domain that will use cryptography and mathematical methods to operate directly on encrypted data. In this way we’ll significantly enhance the data’s privacy and security. One of qTerm’s main purposes is to perform as a telemedicine device which communicates with its AI data center. User’s vital information will be sent via a web widget and HE based technology will ensure a high level of data security. AI systems require robust security mechanisms by their nature and by using HE we are preserving data privacy starting at the source. Particularly, with a telemedicine type device, like qTerm, the data will be encrypted and outsourced to its data center environment for processing, all while encrypted. In the past few years there is a constant growing concern about data privacy and security, and implementing new techniques and methods in this domain will ensure highly secured AI operation and computation. We believe that this is especially important for the qTerm’s device as it collects, processes and records sensitive personal and medical information,” said Danny Rittman the Company’s CTO.
