As we enter a new age of blockchain technology, the demand for greater privacy and security solutions has grown in tandem. Given the issues above, one of the most promising solutions to tackle these issues would be Decentralized Confidential Computing (DeCC). DeCC marks a significant step forward towards a decentralized paradigm on how to process and secure data. Shielding sensitive data with TEE ensures that computations can be performed on encrypted data without ever revealing the underlying information. This article will explain DeCC in plain English, by breaking down its guiding principles, most important technologies, and possible effects on a range of industries.
Introduction to Decentralized Confidential Computing
Decentralized Confidential Computing (DeCC) is an emerging field that focuses on performing computations on data while preserving its confidentiality and integrity in a decentralized environment. DeCC employs cutting-edge cryptographic techniques to keep sensitive information private. It’s different from prevailing computing models because it protects data security during all stages of computation—not just at the beginning and end. Blockchain technology is built on the idea of transparency as a foundational tenet. Yet, privacy is still a deeply important issue.
DeCC uses an innovative blend of cryptographic techniques, hardware approaches, and decentralized technologies to help it do so. These technologies include Zero-Knowledge Proofs (ZKPs), Multi-Party Computation (MPC), Garbled Circuits (GC), Fully Homomorphic Encryption (FHE), and Trusted Execution Environments (TEEs). DeCC is working on improving the integration of cutting-edge technologies with the goal of creating a secure, private computing environment. This uniquely protected environment is very difficult to hack and alter. The main driving motivation of DeCC is to perform computations on sensitive data without requiring trust. This model allows us to address privacy concerns while still enjoying the fruits of decentralization.
Through this, DeCC is set to spark exciting new industries by allowing powerful new applications once thought impossible because of privacy issues to thrive. In healthcare, DeCC enables researchers to study patient data without compromising security. This guarantees the anonymity of each patient’s identity. In private finance, it can enable private transactions and privacy-preserving smart contracts so that no sensitive financial information needs to be public. In supply chain management, DeCC can allow for secure tracking of goods and materials, for example, without exposing businesses’ proprietary information to their competitors. The demand for privacy and security is far outpacing the supply. For these reasons, DeCC will be an important part of ensuring that the future of decentralized technologies is just and equitable.
The Evolution of Data Computation and Security
The history of data computation and security has always been a game of tug-of-war between access and security. In the early computing days, most of data processing took place in centralized, controlled environments. Security was solely reliant on on premise access controls and relatively low barrier authentication measures. As time went on, new requirements called for more advanced security measures. Encryption quickly emerged as an essential measure for protecting sensitive data in transit and at rest. It did not address the lack of data protection during the computation process itself.
The rise of cloud computing brought with it new challenges and opportunities in the data security landscape. High level conclusion Even though cloud providers provide different security services and solutions, it’s still the user’s responsibility to protect the data. New techniques have come on the scene, such as homomorphic encryption and secure enclaves. These advances, collectively known as homomorphic encryption, allow complex computations to be performed on encrypted data without ever revealing the underlying data. These technologies together have cleared the path to Decentralized Confidential Computing (DeCC). It combines the best of decentralized systems with advanced security and privacy tools.
DeCC is a big leap forward the evolution of data computation and security. This development builds upon DeCC’s use of cryptographic techniques and decentralized protocols to enable trustless computations on sensitive data. By implementing this approach, privacy is protected and the advantages of decentralization can be enjoyed. This is unlocking many new applications and use cases. Industries such as healthcare, finance, supply chain management, and government stand to gain immensely. At the same time, the demand for privacy and security is increasing rapidly. Given this, DeCC will be playing a pivotal role in determining the future of data computation.
The Importance of Privacy in Data Processing
Privacy is currently the most important consideration in data processing, primarily for three reasons. First, as a matter of principle, people have an inherent right to determine what happens with their private data. The real ability to choose what information is taken, for what purpose, and what happens with it after that is a critical part of keeping people autonomous and dignified. The second reason privacy is important is because it increases trust between the public and private sector. When people have trust that responsible data governance practices are being followed, they have the confidence. This trust empowers their online participation, research, information sharing, and overall engagement in the digital economy.
Additionally, privacy is important because it helps secure sensitive data from interception and exploitation by third parties. Data breaches and privacy violations hurt people. Data breaches have immediate and long-term effects, including financial loss, reputational damage, and identity theft. In the worst case, they can even endanger public safety and security. As data grows in both its value and ubiquity, the problem demands strong privacy solutions more than ever.
Decentralized Confidential Computing (DeCC) aims to keep data confidential, secure, and private while processing. It permits complex computations to be performed on encrypted data, never revealing the raw data itself. This protects sensitive data while it is being computed on throughout computation. It pangs in particular in decentralized environments where belief is often tenuous. DeCC builds on cutting-edge cryptographic primitives such as Zero-Knowledge Proofs (ZKPs), Multi-Party Computation (MPC), and Fully Homomorphic Encryption (FHE). This empowerment gives them control to remain in command of their data while still pursuing data-driven activities and innovations—all without losing their privacy.
Comparing Traditional Data Processing and Decentralized Confidential Computing
The paradigm of traditional data processing has largely been one where data is ingested into a centralized system to be stored and processed in one place. This method provides a host of benefits including simple oversight, scalability and cost savings. While this is undeniably positive, it poses substantial security and privacy threats. It’s a hard fact that centralized systems are always at risk for single points of failure which can be easily targeted by hackers and other bad actors. Data stored in centralized systems is extremely vulnerable to government surveillance and legal requests. This level of scrutiny undermines the privacy of civil society and academic actors.
Decentralized Confidential Computing (DeCC) represents a paradigm shift when it comes to data processing, providing solutions to the shortcomings of traditional central processing systems. In a DeCC world, the data is being exchanged and aggregated among many nodes or stakeholders. They do so by running those computations through cryptographic techniques that provide privacy. This removes the need for a central server and thus prevents unauthorized data breaches and access. DeCC increases public transparency and accountability by promoting greater control over data processing. Any individual or organization can independently confirm that their data is used as permitted under the specified terms.
Though DeCC has distinct benefits to security and privacy, obstacles still remain. In practice, implementing DeCC solutions is often more difficult and expensive than conventional data processing systems. Operating DeCC might need specific hardware and software, and an understanding of cryptography and decentralized technologies. The appetite for privacy and security is expanding exponentially. Ultimately, the advantages of DeCC will outweigh the negatives, and it will be a more attractive proposition for organizations that handle sensitive data.
Vulnerabilities in the Data Lifecycle
The data lifecycle covers every aspect of data processing, including when, where, and how information is created or collected and later stored, analyzed, and deleted. Each stage brings with it distinct vulnerabilities that can threaten the security and privacy of data. When sensitive information is shared during data collection, it risks being intercepted or stolen by bad actors. Like any data stored electronically, these efforts are susceptible to hacking, data leakage, and physical destruction. It’s true that any kind of data analysis can reveal sensitive information when not done with strong privacy protections in place. Deletion of data is not an infallible safeguard against all possible breaches since deleted data remains susceptible to recovery by malicious actors.
Meanwhile, traditional security technologies such as encryption and access controls can further mitigate some of these vulnerabilities. They are not always the full answer to protecting public health. Unlike other uses of encryption which protect data at rest and in transit, encryption for computation does not have a user-friendly counterpart. Access control mechanisms restrict the individuals/agents who can access data, but they do not stop authorized users from misusing data or inappropriately disclosing sensitive data. Most traditional security measures are employed in centralized, system-controlled environments. These systems are not only vulnerable to single points of failure, but can be easily exposed to insider threats.
Decentralized Confidential Computing (DeCC) addresses vulnerabilities across the data lifecycle. It enables complex computations to be performed on encrypted data, which means the sensitive underlying information never needs to be revealed. This method protects sensitive data at all phases of its lifecycle. It’s particularly powerful in decentralized systems where trust is difficult to come by. Through the use of cutting-edge cryptographic techniques, DeCC strengthens data security and privacy. These technologies require innovations such as Zero-Knowledge Proofs (ZKPs), Multi-Party Computation (MPC), and Fully Homomorphic Encryption (FHE).
Limitations of Transparency in Cryptocurrency
Transparency is often promoted as the great virtue of cryptocurrency and blockchain technology. The same transparency baked into these systems is a double-edged sword that poses serious risks to privacy. This means that anyone with an internet connection has the ability to view all completed transactions on a public blockchain. This openness means that sensitive information such as transaction amounts, wallet addresses, and smart contract code are readily available and susceptible to analysis. This is problematic not only because it would punish responsible individuals and organizations that wish to engage in more private financial activities.
Pseudonymous addresses have limited privacy by design. They are not intrinsically private. It is possible to associate blockchain addresses with real-world identities through sophisticated analysis methods. This is especially true when users are reusing addresses or interacting with centralized exchanges. The transparency of smart contract code can expose vulnerabilities and trade secrets, making it easier for malicious actors to exploit them.
DeCC solves the transparency problem of cryptocurrency by making all transactions and smart contracts private and decentralized. With DeCC, users are able to transact and interact with smart contracts without exposing private information. It does so by utilizing cutting-edge cryptographic primitives such as Zero-Knowledge Proofs (ZKPs), Multi-Party Computation (MPC), and Fully Homomorphic Encryption (FHE). This significantly increases the privacy and security of individual cryptocurrency transactions to unlock the technology for far more use cases.
Key Technologies in Decentralized Confidential Computing
Decentralized Confidential Computing (DeCC) employs a unique combination of cryptographic techniques and hardware solutions. It uses these same decentralized protocols to achieve its goals. These technologies work together to provide a confidential and private computing substrate. They are steadfast pillars against unlawful intrusion and tampering. At the heart of DeCC’s operations are a few key technologies. These are Zero-Knowledge Proofs (ZKPs), Multi-Party Computation (MPC), Garbled Circuits (GC), Fully Homomorphic Encryption (FHE), and Trusted Execution Environments (TEEs).
These technologies can all work together. By layering them together in different configurations, you can create multiple levels of security and privacy. With ZKPs, it is possible to prove that certain computations were properly carried out on encrypted data. MPC shares calculations among different parties in a way that none of the parties has access to the original data. FHE lets you compute on encrypted data while keeping the data encrypted and safe from exposure. At the same time, TEEs provide a trusted space to perform sensitive and complex computations.
The specific technologies that should be used in a DeCC system can vary based on the needs of the particular application. When procuring new technologies, give some thought to what your security and privacy baseline should be. Consider their performance overhead and implementation complexity. DeCC technology is rapidly advancing. Just as it advances, new and better techniques will certainly come along, greatly multiplying the security and privacy guarantees of decentralized systems.
Zero-Knowledge Proofs (ZKP)
Zero Knowledge Proofs (ZKPs) are a novel and powerful cryptographic technique. They allow one party, called the prover, to prove to another party, the verifier, that a statement is true without revealing any knowledge from the statement’s truth. Put another way, the verifier cannot learn anything except that the statement is true. ZKPs have a protected history and enormous significance for cryptography and pc science. They have a wide range of applications including authentication, privacy-preserving data sharing, secure computation and many more.
This is just another instance where ZKPs are extremely valuable, particularly within DeCC. They allow anyone to verify the correctness of any computation without learning anything about the inputs or output of that computation. This is crucial for enabling privacy in decentralized ecosystems where users cannot always trust each other. ZKPs can prove that a smart contract was executed correctly. How They’re Doing It All of this is done while ensuring complete contract and data confidentiality.
There are a number of different flavors of ZKPs, with different advantages and disadvantages. The most popular ZKP techniques are zk-SNARKs, or Zero-Knowledge Succinct Non-Interactive Argument of Knowledge. Two other popular alternatives are zk-STARKs, or Zero-Knowledge Scalable Transparent Argument of Knowledge, and Bulletproofs. Select a ZKP technique most aligned with your application’s unique requirements. Think about what minimal level of security you really need, the performance overhead you’re willing to accept, and how difficult the implementation will be.
Multi-Party Computation (MPC)
Multi-Party Computation (MPC) is an especially exciting cryptographic technique. It allows multiple, mutually distrusting parties to jointly compute a function on their private inputs, without revealing these inputs to each other. Each actor in the process contributes to the result. All that they know is what the output of that single function tells them, there’s no way for them to learn about each other’s data. MPC is widely used in cryptography and computer science for various applications, including secure auctions, private data analysis, and secure voting.
MPC plays a starring role in Decentralized Confidential Computing (DeCC). It allows for the sharing and transfer of complex computations among different parties with the guarantee that the original data cannot be viewed by any one party. This greatly improves the security and privacy of computations in decentralized settings where users and operators cannot trust each other. MPC allows several banks to jointly calculate on their collective customer data. This process ensures that the unique data for each bank remains confidential and secure.
There are dozens of different MPC protocols, each with their strengths and weaknesses. The three most known and utilized MPC protocols are Yao’s Garbled Circuits, GMW (Goldreich-Micali-Wigderson) and BGW (Ben-Or-Goldwasser-Wigderson). Selecting an MPC protocol for the task Choosing the best MPC protocol depends on the specific requirements of the application at hand. Take into account the number of stakeholders, the complexity of the task, and the needed security level.
Garbled Circuits (GC)
Garbled Circuits (GC) is one of the most potent cryptographic techniques. It allows one party, called the garbler, to construct a garbled circuit. This private circuit can be tested by a third party, known as the evaluator, without revealing information about the circuit or the inputs of the circuit. The garbler then encrypts the entire circuit and each input. In the background, the evaluator performs calculations on this encrypted data without ever learning anything about the original data or the circuit. GC is an important primitive in modern cryptography. Even more remarkable, it was the first solution ever proposed to be able to perform computations on encrypted data.
GC becomes extremely valuable in DeCC (Decentralized Confidential Computing). It allows calculations to be performed on encrypted data, without any of the parties knowing what the data is, or what the calculations are. This extends the confidentiality and integrity of all computations, protecting them even in decentralized contexts where trust cannot be assumed or enforced. GC does not require sensitive data within the parties’ control to be disclosed to a third party, enabling secure private computation. This approach does not require any of the parties to unveil the data or computational process to each other.
The complete process of generating and evaluating a garbled circuit spans multiple steps. First, the garbler has to create a circuit that represents the computation they want to perform on the input. Next, the garbler uses a cryptographic technique to encrypt the circuit and the inputs. The evaluator gets the encrypted circuit with the inputs. They then run the computation while both the underlying data and the circuit used for computation remain fully private. GC is a promising tool for secure computation, however it is computationally expensive, especially for large circuits.
Fully Homomorphic Encryption (FHE)
Fully Homomorphic Encryption (FHE) is a revolutionary cryptographic technique. It allows you to run calculations on encrypted data without having to decrypt it first. FHE lets you compute on encrypted data. You can analyze and use this data to do things, but never share it in a way that reveals anything about the original data. This is a major breakthrough in cryptography because it facilitates secure, privacy-preserving computation on sensitive data. FHE provides a foundation for fully encrypted transactions and smart contracts on blockchains.
FHE is particularly impactful when it comes to Decentralized Confidential Computing (DeCC). This allows such computations on encrypted data to take place in a decentralized environment, maintaining privacy of the underlying data from all parties in the calculation. This increases computational security and privacy in decentralized settings where trust is scarce. One such use case is financial applications, where FHE allows for private computations to be performed on collaborative financial data from multiple organizations. This entire process shrouds this data in secrecy for all parties at the table.
Although FHE is the most powerful tool for secure and privacy-preserving computation, it is a highly computing intensive technique. FHE operations are still one thousand to one million times slower than plaintext operations. The numerical speed-up depends on the computation and scheme applied. Ongoing research and development are focused on improving the efficiency of FHE, making it more practical for real-world applications. FHE technology is changing so fast. It is poised to be central in Decentralized Confidential Computing and other domains that value privacy and security.
Trusted Execution Environments (TEE)
Trusted Execution Environments (TEEs) are secure areas within a main processor that ensure confidentiality and integrity of computation. TEEs are generally deployed through hardware security features, like Intel SGX (Software Guard Extensions) and ARM TrustZone. These features enable developers to build sandboxed execution contexts. In these highly controlled environments, sensitive data and code are safe from any form of unauthorized access or malicious code injection. TEEs ensure confidentiality and integrity of computation with low performance overhead.
TEEs do incredibly well in the case of DeCC. They not only allow for highly sensitive computations to be executed securely, they do so even in a decentralized manner. This significantly improves security and privacy of the computations in decentralized settings, like federated learning, where trust is scarce. TEEs provide a means to execute smart contracts on a blockchain in a trustless way while maintaining data privacy properties. They shield the contract code and data from unauthorized access.
TEEs are not without their limitations. They remain susceptible to hardware attacks and side-channel attacks, which can all be exploited to undermine the TEE’s security. TEEs depend on the trustworthiness of the hardware manufacturer, which may be a point of concern for some users. With those limitations in mind, TEEs remain a tremendous tool for secure computation. Along the way, they become ubiquitous in things like mobile security, digital rights management, and cloud computing.
Hybrid Approaches in Decentralized Confidential Computing
Hybrid approaches in Decentralized Confidential Computing (DeCC) pool several cryptographic techniques with hardware solutions. This potent combination takes security and privacy to the next level. These creative strategies leverage the advancements in autonomous, connected and electrified technologies. In so doing, they transcend the sum of their individual parts and provide a bolder, more thoughtful solution. Zero-Knowledge Proofs (ZKPs) are fundamental in allowing anyone to verify multiple computations. They work with Multi-Party Computation (MPC) to verify the accuracy of calculations performed on encrypted data.
A second example of the hybrid approach at work is through the use of Fully Homomorphic Encryption (FHE). This approach is a natural fit with Trusted Execution Environments (TEEs). Fully homomorphic encryption allows you to do complex computations on encrypted data. TEEs provide a trusted environment to execute sensitive computations safely. Through the integration of these tech, we can ensure privacy and security in a decentralized manner.
Hybrid approaches are gaining traction in DeCC as developers look to build safer, privacy-preserving applications. You need to be able to keenly identify the strengths and weaknesses of different cryptographic techniques and hardware solutions. You need the imagination and ingenuity to put these moving pieces together. Like all technologies, deCC is changing quickly. It’s clear that hybrid approaches are going to be central in determining the future of decentralized computing.
Composable DeCC Stacks
Composable DeCC stacks are designed to let you mix and match different Decentralized Confidential Computing (DeCC) technologies. This powerful, modular and flexible approach allows you the freedom to craft unique solutions, tailored to the needs of specialized use cases. This composability gives developers the power to keep harnessing the strengths of different technologies. They can then adapt their DeCC stacks to meet the unique requirements of their applications. Confidential computing technologies are broadly compatible. You can’t just throw the latest and greatest tools here and there. You can’t simply go off and use all the tools, like ZKPs, MPC, GC, and FHE.
A composable DeCC stack for instance might use Fully Homomorphic Encryption (FHE) so computations can be done on encrypted data. It may include Zero-Knowledge Proofs (ZKPs)—cryptographic proofs that those computations are correct—and Multi-Party Computation (MPC), which divides the computations among multiple entities. This composable stack provides strong security and privacy out of the box. It scales phenomenally, allowing you to work with massive datasets.
The process of building out more composable DeCC stacks has only started to take form. It’s promising enough that we believe it’s the future of decentralized computing. With each new DeCC technology developed, developers are picking up useful lessons learned. The result of this trend will be a rapid proliferation of composable DeCC solutions.
