Most digital security systems today focus on protecting data while it’s stored (data at rest) and while it’s being transmitted over a network (data in transit).
However, there’s still one stage of concern: when data is being processed or used by an application (data in use).
To address this, confidential computing has emerged, a technology designed to keep data secure even while it’s being processed.
This technology is increasingly being applied in cloud computing, artificial intelligence (AI), blockchain, and various services that require a high level of privacy.
In this article, we’ll discuss what confidential computing is, how it works, and why it’s increasingly important in the future of digital data security.
What Is Confidential Computing?
Confidential computing is a security approach that aims to protect data while it’s being used or processed (data in use).
This technology is supported by major companies such as Google Cloud, IBM, and the Confidential Computing Consortium.
This technology complements existing protections, namely data encryption while it’s stored (data at rest) and data encryption while it’s being transmitted (data in transit).
The primary focus of confidential computing is keeping data secure during the computing process.
With this technology, sensitive data is processed in an isolated environment, preventing unauthorized parties from accessing it, including when the data is being used by applications or systems.
Why Does Data Need to be Protected While in Use?
Generally, data security efforts are applied more when information is in storage (data at rest) or while being transmitted over a network (data in transit).
However, data is at its most vulnerable when it is being processed or used (data in use). The following are the main reasons why data must be protected while in use.
1. Three States of Data in Digital Systems
Data in digital systems generally exists in three states. Data at rest is data that is stored, such as files on a server or database. Data in transit is data that is being transmitted over a network, for example when sending an email.
Meanwhile, data in use is data that is being processed, such as when a banking application processes a transaction or an AI analyzes information.
2. Security Challenges in Data in Use
When data is processed, it usually must be unlocked or decrypted before it can be read by the system. This situation creates a security gap because data is no longer protected by encryption during the computing process.
3. Why Is This Important?
Today, more and more sensitive data is processed through the cloud, AI, and modern digital services. Therefore, protecting data in use is becoming increasingly relevant to maintain information confidentiality during the data processing process.
How Does Confidential Computing Work?
Confidential computing works by processing data in a secure and isolated environment so that sensitive information remains protected during use. Here’s how it works.
1. Trusted Execution Environment (TEE)
Most confidential computing technologies use a Trusted Execution Environment (TEE), which is a dedicated computing area separate from the main system.
This area serves to protect data and computing processes from unauthorized access.
2. Isolated Data Processing
Data is processed in a protected environment so that its contents cannot be accessed or viewed by parties outside the area. This way, data confidentiality is maintained throughout the process.
3. Additional Protection against Internal Threats
In addition to protecting against external threats, confidential computing also helps reduce the risk of unauthorized access from internal parties.
Even those managing the infrastructure or system cannot easily see the data being processed.
What is the Difference Between Confidential Computing and Regular Encryption?
Although both aim to protect data, traditional encryption and confidential computing have different focuses. Here are some differences between the two.
1. Traditional Encryption
Traditional encryption protects data at rest and in transit. However, data usually needs to be decrypted before it can be used or processed by the system.
2. Confidential Computing
Confidential computing focuses on protecting data in use. This technology helps maintain data confidentiality during the computing process.
3. Why Do They Complement Each Other?
Confidential computing is not a substitute for encryption. Encryption protects data during storage and transmission.
Meanwhile, confidential computing adds protection during data processing. The combination of the two creates more comprehensive data security.
What is the Relationship Between Confidential Computing and Blockchain?
In the blockchain and Web3 ecosystems, confidential computing is used to enhance data privacy during the computing process. Here are some of the relationships between confidential computing and blockchain.
1. Data Protection in Smart Contracts
Several blockchain projects are exploring confidential computing to help protect sensitive data used in smart contracts.
The goal is to enhance privacy without disrupting the automated processes executed by smart contracts.
2. Privacy in Web3 Applications
Confidential computing can also help protect sensitive user information in decentralized or Web3 applications. This way, data remains more secure when processed by the system.
3. Privacy Based Blockchain Development
A number of blockchain projects combine privacy technologies with confidential computing, such as the Oasis Network, Secret Network, and Phala Network. In general, this approach aims to enhance data confidentiality in a blockchain environment.
What is the Relationship Between Confidential Computing and AI?
As the use of AI becomes more widespread, data protection has become a key focus in its development. Here are some of the connections between confidential computing and AI.
1. Protecting AI Training Data
AI models often process large amounts of sensitive data. Confidential computing helps maintain the confidentiality of this data while it is being used and processed by AI systems.
2. Supporting More Secure AI
Data security has become a critical issue in the development of modern AI. Therefore, confidential computing is beginning to be utilized to help improve data protection during the computing process.
Advantages and Challenges of Confidential Computing
Confidential computing offers additional protection for data being processed, but its implementation also presents several challenges. Here are some of its advantages and challenges.
Advantages
Confidential computing helps protect data while in use, enhances privacy, and supports more secure cloud computing environments. This technology is also increasingly relevant for various modern needs, including AI and blockchain.
Challenges
The implementation of confidential computing is still relatively complex and requires specific hardware support. Furthermore, its adoption is still developing, so it is not yet widely used in all digital systems and services.
Examples of Confidential Computing Uses
Confidential computing is starting to be used in various sectors that require high levels of data protection during the computing process, including the following:
1. Cloud Computing
In cloud computing services, this technology enables companies to process sensitive data more securely without exposing that information to unauthorized parties.
2. Financial Services
In the financial sector, confidential computing can help protect transaction data, customer information, and various other sensitive data while it is being processed by the system.
3. Blockchain and Web 3
In the blockchain and Web 3 ecosystems, this technology supports applications that require a higher level of privacy, especially when managing and processing user data.
4. Artificial Intelligence
In AI development, confidential computing helps maintain the security of data used for model training and inference processes, so that sensitive data remains protected while being used by AI systems.
Why Will Confidential Computing Become Increasingly Important in the Future?
Global data volume continues to increase with the rapid development of AI, cloud computing, blockchain, and Web 3.
As a result, the need for data protection is also increasing, especially while data is being processed by systems.
Because it can protect data during the computing process, confidential computing is seen as a technology that can help address future data security and privacy challenges.
Conclusion
So, that was an interesting discussion about confidential computing as a technology that protects data during use, which you can read more about in the Crypto Academy at INDODAX Academy.
In conclusion, confidential computing is here to address one of the biggest challenges in digital security: protecting data while it’s being used or processed.
While traditional encryption focuses on securing data during storage and transmission, this technology adds a layer of protection to the data-in-use phase, which has historically been the most vulnerable area.
The development of AI, cloud computing, blockchain, and Web 3 has increased the need for sensitive data processing.
In this context, confidential computing offers a new approach that allows data to remain protected during the computing process.
Because of this, this technology is starting to be seen as a crucial foundation for building more secure, private, and trustworthy digital services in the future.
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FAQ
- What is confidential computing?
Confidential computing is a security technology that protects data while it is being processed or used by a computer system. - What is data in use?
Data in use is data that is being actively used or processed by an application or computer system. - What is the difference between confidential computing and traditional encryption?
Traditional encryption protects data while it is stored or transmitted, while confidential computing focuses on protecting data while it is being processed. - How does confidential computing relate to blockchain?
This technology can be used to enhance data privacy in smart contracts, Web3 applications, and some privacy-focused blockchains. - Why is confidential computing important for AI?
Because AI often processes large amounts of sensitive data, it requires additional protection during the computational process.
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