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We know that since its inception the data stored on blockchain technology is quite transparent and publicly visible. Several efforts have been made to address the lack of data confidentiality and introduced solutions using a trusted execution environment or zero knowledge. But, none of them have truly hit the target.
One may argue that blockchain technology aims to be transparent and avoid centralization. So why do we need solutions that provide the ability to control who can see your data? Initially, the argument often overlooked the distinction between public blockchains, which are fully decentralized and transparent, and permissioned (or private) blockchains, which are designed for specific use cases where control over access is necessary. For this, Zcash was a first-of-its-kind attempt followed by another bunch of teams having different approaches for private blockchains. This led to a market segment which is now around $16 billion in terms of market capitalization.
The latest argument suggests that blockchain technology can be designed to offer selective transparency. This means that while the ledger may be transparent and immutable, the details of specific transactions or data can be encrypted and made accessible only to authorized parties. This approach supports privacy-sensitive applications like healthcare records or confidential financial transactions, where information needs to be both secure and private.
So, there are a lot of attempts to achieve on-chain confidentiality. One of the recent solutions brought to the table is Homomorphic Encryption (FHE).
What is Homomorphic Encryption (FHE)?
Homomorphic encryption is a sophisticated encryption method that enables computations on data while it remains encrypted, eliminating the need for decryption. This approach ensures that sensitive data can stay encrypted during processing, and analysis, and while being subjected to various algorithms. Consequently, it offers the privacy of data while allowing it to be shared with third parties for computational purposes. This is a significant advancement over traditional encryption techniques, which do not support computations on encrypted data, paving the way for numerous promising applications.
Why does homomorphic encryption represent a game-changing technology? Consider the scenario where you wish to use a service to evaluate your genetic makeup for potential hereditary disease risks. With most current services, this would involve granting a third party full access to your genetic information, including any medical conditions you might have or are predisposed to. Homomorphic encryption, however, could allow you to leverage the full advantages of such services without revealing any of your personal genetic information.
Use-Cases of Homomorphic Encryption (FHE)
Enhanced Privacy in AI/ML Deployment
Artificial Intelligence and Machine Learning applications can leverage encrypted datasets for training purposes, eliminating the need for direct exposure of sensitive information. This approach ensures the protection of data privacy, encouraging a wider audience to contribute their personal information for AI initiatives. Such contributors are vital for the development of AI technologies that have a significant impact in real-life scenarios.
Enhanced Security in Cloud Computing
Unlike conventional cloud computing, which operates on plain text data, advanced encryption technologies allow for computations on encrypted data. This means that sensitive information remains secure from both cloud service providers and potential security breaches, with the data owner being the only party to decrypt the final output.
Adherence to Privacy Regulations
The introduction of data protection laws like GDPR has imposed significant privacy obligations on businesses worldwide. Advanced encryption techniques, such as homomorphic encryption, offer businesses the means to comply with these regulations by securely processing user data online without compromising privacy.
Innovations in Secure Voting
The application of encryption in voting systems can safeguard the anonymity of voters, potentially leading to a higher participation rate due to increased trsut in the electoral process. Techniques like Paillier encryption can sum votes while keeping individual choices confidential, also allowing for the verification of the final tally without compromising voter privacy
Enhacing Supply Chain Confidentiality
For organizations that share critical data with external partners for supply chain management, traditional practices pose risks to data security. Implementing encryption management, traditional practices pose risks to data security. Implementing encryption within these systems enables secure data sharing and processing without exposing sensitive information, thereby protecting against potential cybersecurity threats.
Disadvantages of Homomorphic Encryption (FHE)
Fully Homomorphic Encryption (FHE) holds considerable promise for diverse applications within the blockchain arena. Yet, its adoption among blockchain initiatives and protocols is minimal. This stems from inherent limitations in current FHE offerings, including:
Enhanced Performance Demand
FHE offers numerous advantages, but its encryption and decryption operations demand significantly higher computational resources compared to traditional encryption methods. This increased demand makes FHE less suitable for blockchain technologies, which already suffer from inefficiencies due to the need for decentralized verification—each node repeats the same processes every time a block is formed.
Increased Ciphertext Volume
FHE methods produce notably large ciphertexts, posing challenges for blockchain systems where block space is highly valuable and limited.
Standardization Deficit
Unlike other encryption methods that have undergone standardization—like AES, which is tailored for specific applications—FHE is a relatively newer encryption form that has yet to achieve similar levels of standardization and accessible algorithms.
Algorithmic Complexity
The complexity of FHE algorithms, coupled with the lack of standardized implementations across different algorithms and programming languages, makes it challenging to apply FHE widely across various platforms.
Closing Thoughts
Source: Zama.ai
Thi graph provides the projection of the growth in Fully Homomorphic Encryption (FHE) adoption, measured by number of projects over time, from 2019 to 2029. It shows a very rapid, almost exponential increase in adoption. Starting from just 2 units in 2019, the graph shows a growth to 11 units by 2021 and then to 37 units by 2023, marking a steady increase. The projected growth becomes steeper after 2023, with a predicted rise to 2,600 units by 2025. The trend continues to ascend sharply, reaching 9,000 units by 2027 and culminating at 23,000 units by 2029.
The acronym “ASIC” at the bottom left corner of the graph refers to a specific type of hardware, Application-Specific Integrated Circuit, which might imply that the projections are related to ASIC developments in the context of FHE. The adoption rate is expected to significantly accelerate from the implementation of ASICs.
