As we move further into the age of big data and artificial intelligence (AI), privacy concerns have become increasingly critical, especially when dealing with sensitive information. The growing reliance on data-driven insights across various sectors has intensified the need to protect individual privacy, particularly when dealing with sensitive information like personal health records, financial data, and biometric information.

Privacy-preserving machine learning (PPML) has emerged as a key field addressing this need by balancing the demand for data insights with the necessity of safeguarding personal privacy. The following sections will delve into various PPML techniques and their applications, exploring how they contribute to data security in AI systems.

This following article discusses -

1. PPML techniques that ensure data security while training AI models on sensitive data.

2. An analysis of various methodologies of PPML including Differential Privacy (DP), Homomorphic Encryption (HE), Federated Learning (FL), and Secure Multi-Party Computation (SMPC)

3. An analysis of the strengths, weaknesses, and the utilitarian potential of each of the methodology specified above.

4. A discussion of the current State of the Art (SOTA) of privacy of data "On-chain" for blockchains and dApps.

5. A brief case study of Aleph Zero building infrastructure for confidential Web3 dApps. (tentative)

Traditional machine learning often necessitates centralized access to large datasets, which poses risks such as data breaches and privacy violations. Centralizing sensitive data makes it an attractive target for malicious actors and raises privacy concerns due to data aggregation. Privacy-preserving machine learning (PPML) addresses these issues by allowing AI model training while keeping individual data points private. Unlike traditional methods, PPML maintains data decentralization and encryption during the training process, significantly reducing the risk of privacy breaches.

PPML encompasses various techniques designed to protect privacy throughout the machine learning pipeline, from data collection to model deployment. These techniques include differential privacy, homomorphic encryption, federated learning, and secure multi-party computation (SMPC). Each method offers a unique approach to preserving privacy while still enabling meaningful data insights.

Differential privacy introduces random noise to data, making it difficult to identify individual data points while still allowing for accurate aggregate analysis. Homomorphic encryption enables computations on encrypted data without decrypting it, ensuring data remains confidential during processing. Federated learning involves training models across multiple decentralized devices or servers holding local data samples, without exchanging them. This approach keeps data localized and secure. Secure multi-party computation (SMPC) allows multiple parties to collaboratively compute a function over their inputs while keeping those inputs private.

The proceeding article delves into an in-depth discussion of the various methods of privacy preservation especially as it concerns to decentralization, AI and blockchain technologies -

Differential privacy is a framework designed to quantify the privacy guarantees of a data analysis algorithm. It involves adding carefully calibrated noise to computations to ensure the output does not reveal information about any single individual in the dataset, offering strong privacy guarantees even against powerful adversaries. An example -

Preserving Privacy using differential privacy -

As the diagram above shows, DP is an attempt to prove that even if the user (X) was NOT to give their data for computation, the outcome would still be relatively the same, as dignified in the diagram above as the Greek “e”.

Principles of Differential Privacy

Differential privacy aims to ensure minimal information about any individual’s data is revealed, even if an adversary has auxiliary information.

Formally, an algorithm satisfies differential privacy if the probability distribution of its output remains nearly the same for any two datasets differing by a single individual’s data. The argument is portrayed using a diagram below followed with a conclusion statement:

````As the diagram above shows, DP is an attempt to prove that even if the user (X) was NOT to give their data for computation, the output would still be nearly the same, as dignified in the diagram above as the Greek “e”.````

Differential privacy has been applied to various machine learning tasks to train models while preserving data privacy. Applications include:

Classification: Differential privacy ensures that trained classifiers do not memorize specific details about training samples by adding appropriate noise to the training process.

Clustering: Differential privacy can perturb distance metrics or cluster assignments, ensuring clustering does not leak information about individual data points.

Data Mining: Algorithms like association rule mining can be modified to add noise to support counts, preserving individual privacy while discovering patterns in data.

Strong Privacy Guarantees: Differential privacy offers rigorous mathematical guarantees for individual privacy.

Flexibility: It can be applied to various data analysis tasks, including machine learning and statistics.

Privacy-Accuracy Tradeoff: Practitioners can adjust the noise to balance privacy and accuracy based on specific requirements.

Challenges

Noise Addition: Noise can degrade result accuracy, especially for fine-grained analysis.

Algorithm Design: Designing differentially private algorithms requires expertise and careful consideration of the task.

Scalability: Differential privacy techniques may introduce computational overhead, necessitating efficient algorithms and optimization.

Homomorphic encryption allows computations on encrypted data without decryption, preserving privacy throughout the computation process. This technique is a popular choice and a powerful method of privacy preservation in PPML, enabling model training and inference on encrypted data while maintaining privacy. In blockchain based protocols, FHE (Fully Homomorphic Encryption) allows complete computation on fully encrypted data from one endpoint to another. This is a popular method utilized in blockchain technologies, and is discussed in further details in the article below.

Homomorphic encryption schemes, such as partially homomorphic encryption (PHE) and fully homomorphic encryption (FHE), preserve the algebraic properties of plaintext data, allowing operations on encrypted data to produce the same results as operations on plaintext data. The diagram below is a clear representation of how HE is executed -

As seen above, data received is encrypted using an asymmetric key (a public and a private key), the public key is used to encrypt the data upon which computation is performed, and the private key is used to release the output of computation back to the user.

Secure Model Training: Models can be trained on encrypted data, allowing collaborative training without data exposure.

Private Inference: Predictions can be made on encrypted inputs without revealing the inputs.

Outsourced Computation: Data can be encrypted, processed by third parties, and results received without exposing plaintext data.

Data Sharing: Enables secure collaborative analysis without revealing underlying information.

Computational Overhead: Homomorphic encryption is resource-intensive, leading to slow computation speeds.

Key Management: Secure key management is crucial to prevent data exposure.

Complexity: Implementing homomorphic encryption schemes requires expertise in cryptography.

Federated learning is a decentralized approach to machine learning that addresses privacy concerns by allowing multiple parties to train a shared model without sharing their data. Each party trains the model on local data and shares only the model updates with a central server.

Initialization: A central server initializes a global model and distributes it to participating devices.

Local Model Training: Each device trains the model on its data.

Model Update: Devices compute and send model updates to the central server.

Secure Aggregation: The server aggregates updates to update the global model.

Iterative Training: This process iterates, refining the model with each round.

*Diagram: A representation of the distributed model of FL. By allowing local data to be computed on a local “home device”, privacy of data is enhanced. In such a way, a global model is trained “locally” and only the updated version of the global modal is forwarded to the next device for local training.*

Privacy Preservation: Data remains on local devices only, preserving privacy.

Data Security: Reduces the risk of data breaches.

Efficiency: Distributes computational load among devices, saving on bundle costs.

Customization: Models can be tailored to local datasets.

Continuous Learning: Incorporates new knowledge with each individual iteration.

Communication Overhead: Transmitting model updates can introduce latency.

Data Heterogeneity: Variations in local datasets can complicate aggregation and wide-spread distribution of data may prevent a smooth data-set.

Security Concerns: Ensuring secure transmission and aggregation of updates is crucial.

Bias and Fairness: Federated learning may exacerbate biases in local datasets.

SMPC is a cryptographic technique enabling multiple parties to jointly compute a function over their inputs while keeping those inputs private. It allows computations on encrypted data, ensuring no party learns anything about others’ inputs.

To explain using an analogy, SMPC enables “black box” functionality where many people can work on a calculation together using their private information. Even though everyone can see the result, their data is kept secret.

A use case example -

````Explanation -

Purpose of SMPC — Computation using multi-party poles without revealing data to each other

Process — Sampel data to be computed — a = x * y * z

Computation tasks are distributed between 3 participating parties. Computational work is performed without any private information being revealed to each other. Communication is administered using an administrator.

Output — Reconstruction of (individual) computation of work into 1 output data in the form of F(a).````

Encryption of Inputs: Parties encrypt their inputs using techniques like homomorphic encryption or secret sharing.

Collaborative Computation: Parties perform computations on encrypted data and share intermediate results.

Result Decryption: Parties collectively decrypt the final result without revealing individual inputs.

Model Training: Parties jointly train models on encrypted data.

Gradient Descent Optimization: SMPC enables secure gradient computation and aggregation.

Prediction and Inference: Allows private predictions on encrypted inputs.

Privacy Preservation: Ensures data points remain private throughout computation.

Data Confidentiality: Guarantees confidentiality of sensitive information.

Collaborative Learning: Enables collaboration without data sharing.

Zero-Knowledge Proofs are cryptographic protocols where one party (the prover) can prove to another party (the verifier) that a statement is true without revealing any information beyond the validity of the statement. The essential properties of ZKPs are:

Completeness: If the statement is true, an honest prover can convince an honest verifier of this fact.

Soundness: If the statement is false, no dishonest prover can convince the honest verifier that it is true, except with some small probability.

Zero-Knowledge: If the statement is true, the verifier learns nothing other than the fact that the statement is true.

A simple analogy to understand ZKPs -

Non-interactive Zero-Knowledge Proofs (NIZKs): A type of ZKP where the proof can be verified by anyone without interaction.

zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge): A popular type of NIZK that is used in blockchain applications like Zcash.

Scalability: PPML techniques often involve complex computations that can be resource-intensive.

Computational Overhead: Techniques like homomorphic encryption and SMPC can slow down computations.

Privacy-Accuracy Tradeoff: Balancing privacy and model accuracy requires careful design and optimization.

Current State Of The Art (SOTA) on “On-Chain” Privacy for Blockchains

How do decentralized protocols ensure the privacy of your data “On Chain”? First, an understanding of WHY data privacy is important and what is preventing it is important:

Current issues with building infrastructure On-Chain -

- Data is completely public.

- Computing and storing data on chains is prohibitive, expensive and is slow to scale.

So the conclusion is — Decentralized applications that lack a scalable solution for data privacy are useless.

Solution? — Enhanced data privacy along with scalability

These privacy-preserving techniques contribute to the decentralization and protection of sensitive data in blockchain systems:

Methods like SMPC and TEEs distribute the computation and verification tasks among multiple nodes, preventing any single point of control or failure. This maintains the decentralized nature of blockchain networks.

Techniques like FHE, ZKPs, and confidential transactions ensure that sensitive data remains private, even when processed on-chain. This is crucial in maintaining user trust and compliance with privacy regulations.

These methods provide mechanisms to verify the correctness of computations and transactions without revealing underlying data. This builds trust in the system while maintaining transparency.

The combination of advanced cryptographic techniques like FHE, ZKPs, and SMPC with blockchain technology represents the current state-of-the-art in ensuring data privacy on-chain.

These methods enable secure, private, and decentralized applications that can leverage AI without compromising user data privacy. As AI continues to evolve, integrating these privacy-preserving techniques into blockchain-based AI model training can unlock new possibilities while maintaining trust and compliance with data privacy regulations.

Cluster Protocol is a Proof of Compute Protocol and Open Source Community for Decentralized AI Models. We are dedicated to enhancing AI model training and execution across distributed networks. It employs advanced techniques such as fully homomorphic encryption and federated learning to safeguard data privacy and promote secure data localization.

Cluster Protocol also supports decentralized datasets and collaborative model training environments, which reduce the barriers to AI development and democratize access to computational resources. Its innovative features, like the Deploy to Earn model and Proof of Compute, provide avenues for users to monetize idle GPU resources while ensuring transaction security and resource optimization.

Cluster Protocol provides an infrastructure to anyone for building anything AI over them. The platform’s architecture also fosters a transparent compute layer for verifiable task processing, which is crucial for maintaining integrity in decentralized networks.

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