Future-Proofing Privacy: Securing AI, LLMs and Data with Homomorphic Encryption
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Privacy-enhancing technologies (PETs) are essential for businesses facing strict data protection regulations, secure third-party data sharing demands, and potential reputation risks from privacy breaches, with cryptographic algorithms and data masking techniques fortifying data privacy even further.
Homomorphic Encryption (HE) represents a transformative breakthrough, allowing complex computations on encrypted data without decryption, ensuring data privacy and security for an increasingly privacy-conscious digital future.
The Relevance of Homomorphic Encryption Today
Sharing private data sets with third parties, such as cloud services or other companies, is a challenge due to data privacy regulations such as GDPR and CCPA. Failure to comply with these regulations can lead to serious fines and damage business reputation.
Traditional encryption methods provide an efficient and secure way to store sensitive data on cloud environments in an encrypted form. However, to perform computations on data encrypted with these methods, businesses either need to decrypt the data on the cloud, which can lead to security problems, or download the data, decrypt it, and perform computations, which can be costly and time-consuming.
Homomorphic encryption enables businesses to share private data with third parties to get computational services securely. With HE, the cloud service provider or the outsourcing company has access only to encrypted data and perform computations on it. These services then return the encrypted result to the owner who can decrypt it with a private key.
Choosing the Right Homomorphic Encryption
Sharing private data sets with third parties, such as cloud services or other companies, is a challenge due to data privacy regulations such as GDPR and CCPA. Failure to comply with these regulations can lead to serious fines and damage business reputation.
Traditional encryption methods provide an efficient and secure way to store sensitive data on cloud environments in an encrypted form. However, to perform computations on data encrypted with these methods, businesses either need to decrypt the data on the cloud, which can lead to security problems, or download the data, decrypt it, and perform computations, which can be costly and time-consuming.
Homomorphic encryption enables businesses to share private data with third parties to get computational services securely. With HE, the cloud service provider or the outsourcing company has access only to encrypted data and perform computations on it. These services then return the encrypted result to the owner who can decrypt it with a private key.
Choosing the Right Homomorphic Encryption
Sharing private data sets with third parties, such as cloud services or other companies, is a challenge due to data privacy regulations such as GDPR and CCPA. Failure to comply with these regulations can lead to serious fines and damage business reputation.
Traditional encryption methods provide an efficient and secure way to store sensitive data on cloud environments in an encrypted form. However, to perform computations on data encrypted with these methods, businesses either need to decrypt the data on the cloud, which can lead to security problems, or download the data, decrypt it, and perform computations, which can be costly and time-consuming.
Homomorphic encryption enables businesses to share private data with third parties to get computational services securely. With HE, the cloud service provider or the outsourcing company has access only to encrypted data and perform computations on it. These services then return the encrypted result to the owner who can decrypt it with a private key.
Choosing the Right Homomorphic Encryption
Homomorphic encryption comes in various types:
The choice of which type of homomorphic encryption to use depends on your application's requirements and the trade-off between security and computational complexity. For basic tasks, partially homomorphic encryption is efficient. Somewhat homomorphic encryption offers more flexibility but comes with operational limitations. Leveled fully homomorphic encryption is ideal for complex operations. Fully homomorphic encryption (FHE) allows unrestricted computations but is computationally expensive.
Select the type that aligns with your specific computational needs, operational constraints, and data sensitivity in your application.
Homomorphic Encryption in Real-World Applications
Homomorphic encryption's transformative power extends to various domains, including secure cloud computation, regulatory compliance, supply chain security and others . In secure cloud computation, traditional methods expose sensitive data to cloud operators, raising security concerns. With homomorphic encryption, cloud servers can process encrypted data.
In an era of stringent data privacy regulations like GDPR, homomorphic encryption emerges as a solution for businesses seeking to provide online services while adhering to regulatory requirements and safeguarding user data. This technology opens doors to regulatory compliance and data protection.
For supply chain security, where sensitive data sharing with contractors and third parties is common, homomorphic encryption plays a pivotal role. It enables companies to mitigate risks by using encrypted data within backend systems, ensuring that necessary actions can be computed for third parties without exposing sensitive information. This application strengthens supply chain security, reducing vulnerabilities..
Speeding Up Homomorphic Encryption: Advances in Software & Hardware
Historical Fully Homomorphic Encryption (FHE) limitations encompassed speed, functionality, and complexity. While there's been a 20x speed boost in recent years, cost-effectively implementing Large Language Models (LLMs) with FHE remains a challenge, with an average-sized LLM demanding one billion programmable bootstrapping (PBS) operations – the most expensive part of FHE. Currently, modern CPUs can handle about 200 8-bit PBS operations per second at a cost of $0.001, which is $5,000 per token. Fortunately, ongoing optimization strategies offer a promising path forward:
The Hardware Acceleration: Cheetah's Approach
One such hardware approach is Cheetah, a solution that obtains 79× speedup through a combination of algorithmic and hardware optimizations, significantly accelerating privacy-preserving deep neural network (DNN) inference.
Privacy-preserving HE inference for complex DNNs, such as ResNet50, can now approach speeds nearly on par with plaintext inference.
New Innovative Hardware Solutions
Other hardware approaches include enhance cybersecurity within computing systems.
Within this program, an array of innovative approaches is being developed to address various aspects of fully homomorphic encryption (FHE) implementation. These approaches encompass data movement, management, parallel processing, custom functional units, compiler technology, and formal verification methods. The primary aim is to optimize FHE implementation, ensuring it's both effective and accurate, while significantly reducing the performance overhead typically associated with FHE computations. The end goal is to create an accelerator that vastly reduces computational run time overhead in comparison to current software-based FHE computations on conventional CPUs.
Efficiency in Software: NVIDIAs Approach
Recently NVIDIA started addressing efficiency challenges in encrypted computing. This end-to-end framework converts C++ algorithms into fully homomorphic encryption (FHE) representations for efficient execution on various GPUs. It comprises three components: a frontend that converts input programs into the encrypted domain, a runtime library for distributing encrypted workloads to GPU workers, and a backend that implements CGGI cryptosystem for encrypted Boolean operations. ArctyrEX optimizes Boolean circuit generation and efficiently coordinates the execution of encrypted gates, enhancing the efficiency of encrypted computing.
Enhancing Privacy with Federated Learning
Federated learning, an emerging technique that enables collaborative model training without data sharing, holds great promise. However, it's not immune to privacy vulnerabilities. To fortify the privacy of this distributed machine learning approach, Homomorphic Encryption (HE) and Differential Privacy (DP) emerge as key solutions. HE facilitates secure computations on encrypted data, while DP ensures strong privacy protection by introducing protective noise into the data.
Other Advancements in Private Inference: CryptoNAS
Other projects are exploring private inference (PI) techniques that allow for secure inferences without revealing sensitive inputs. CryptoNAS, a Neural Architecture Search (NAS) method tailored for private inference is one such solution.
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CryptoNAS introduces the concept of a "ReLU budget" to measure inference latency and constructs models that maximize accuracy within a budget. CryptoNAS improving accuracy by 3.4% while reducing latency by 2.4 times compared to current state-of-the-art methods.
This is just a small example of some of the advancements that are being made.
The ML/AI Homomorphic Encryption Landscape
Privasea AI
Privasea AI Network is a privacy-preserving machine learning initiative, harnessing the potential of Fully Homomorphic Encryption (FHE).
This project serves as a bridge between user data and distributed computing power, with a primary focus on security. FHE, the core technology, empowers arbitrary computations on encrypted data, ensuring that sensitive information remains shrouded in privacy. Privasea utilizes FHE to enable users to securely upload their encrypted data to the platform's storage layer, which is then transferred to distributed computing nodes for processing. The data remains encrypted throughout its journey, with decryption restricted solely to the user, guaranteeing unparalleled security and privacy.
In addition to security, Privasea AI Network offers comprehensive functionality for machine learning tasks, with lower communication requirements compared to other secure computation methods. Users can engage with the platform without concerns about online constraints, and the project supports a wide array of machine learning algorithms, even allowing users to upload their preferred models. This empowers users to leverage the latest machine learning techniques while safeguarding the security of their data.
Concrete ML, Zama's latest addition to their suite of Fully Homomorphic Encryption (FHE) tools, introduces a powerful open-source machine learning inference framework that seamlessly merges artificial intelligence with FHE technology. The significance of Concrete ML lies in its ability to empower traditional data scientists and AI engineers to incorporate FHE into their machine learning models without requiring prior expertise in cryptography. Developers can continue to leverage familiar tools like scikit-learn and PyTorch, as they integrate smoothly with Zama's open-source framework.
At a high level, using Concrete ML to transform a standard machine learning model into one capable of making inferences on encrypted data involves several key steps. First, the model is trained on unencrypted data, just as one would with conventional machine learning models. Then, Concrete ML facilitates the implementation of a quantization scheme, converting decimals and floating-point values into integers. This step is critical since Concrete ML exclusively works with integers. Finally, Zama's compiler takes care of the conversion, turning the standard program code into one capable of interpreting encrypted data, ensuring compatibility with FHE.
Open Source Obfuscation for LLMs
OpaquePrompts, developed by Opaque Systems, is an open-source solution on GitHub designed to address privacy concerns in AI applications, particularly Large Language Models (LLMs). It ensures the privacy of user data by removing sensitive information before interaction with the LLM. The workflow involves processing user input, identifying sensitive data, sanitizing prompts, interacting with LLMs, and finally, restoring the original data in responses. This tool is tailored for scenarios requiring insights from user-provided contexts while prioritizing data privacy and confidentiality.
Here are a few other notable companies operating in this space:
These companies are pushing the boundaries of privacy and security, demonstrating the ever-growing importance of PETs, especially HE, in an increasingly data-driven world.
The Future of Data Privacy
Privacy-enhancing technologies (PETs) & Homomorphic Encryption are taking the spotlight, acting as the linchpin that connects data and distributed computing while fortifying security.
As AI and LLMs continue their rapid expansion, the imperative for secure AI grows exponentially, and these cutting-edge solutions and advancements are setting a solid foundation for a world where privacy and security coexist, ensuring the AI-driven future remains steadfastly safeguarded.
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References:
Recent Advances in Artificial Intelligence and Tactical Autonomy: Current Status, Challenges, and Perspectives
Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference
Data augmentation using Heuristic Masked Language Modeling
CryptoNAS: Private Inference on a ReLU Budget
Identifying and Mitigating the Security Risks of Generative AI
A systematic review of homomorphic encryption and its contributions in healthcare industry
The Future of Fully Homomorphic Encryption
DeepReDuce: ReLU Reduction for Fast Private Inference