Integrated Cyber Solutions Inc., also known as Integrated Quantum Technologies, has released a pivotal white paper authored by Mr. Jeremy Samuelson, the company's Executive Vice President of AI and Innovation.
This document introduces the VEIL™ (Vector Encoded Information Layer) architecture, a framework aimed at fostering privacy-preserving machine learning to handle sensitive data. The white paper is accessible on arXiv, a renowned open-access research repository operated by Cornell University, and has received endorsement from Dr. Mohammad Tayebi, Assistant Professor at Simon Fraser University.
Supervised Machine Learning Framework
The white paper, titled "Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning," is now publicly available on the arXiv platform. It showcases the VEIL™ framework, which addresses the challenges of existing privacy-preserving techniques like homomorphic encryption and differential privacy that may hamper computational efficiency and predictive performance.
Multi-objective Encoder and Anonymization
The research introduces Informationally Compressive Anonymization (ICA), advocating for a supervised, multi-objective encoder that operates within a secure environment.
This encoder processes raw inputs into anonymized, low-dimensional representations, which can then be utilized in machine learning tasks while the source data remains protected. The paper asserts that these outputs are structurally non-invertible, preventing reconstruction of the original inputs.
Advancing Predictive Utility
Distinct from traditional privacy methodologies that rely on cryptographic and noise injection techniques
Distinct from traditional privacy methodologies that rely on cryptographic and noise injection techniques, the VEIL™ architecture purportedly maintains or enhances predictive utility by integrating representation learning with downstream objectives. The framework employs various constraints to ensure data protection, as evidenced by experimental data indicating maintained or improved predictive outcomes without the typical constraints of computational overhead or scalability.
With a theoretical foundation grounded in topological and information-theoretic principles, the paper details the non-invertibility of encoded outputs under hypothetical attacker scenarios, suggesting reconstruction is practically implausible. It further explores how dimensionality reduction, coupled with increasing attacker uncertainty, diminishes the likelihood of data reconstruction.
Separation of Data Environments
The VEIL™ architecture delineates clear separations between source, training, and inference environments to safeguard raw sensitive data while allowing for the application of encoded representations in machine learning workflows. It also addresses deployment strategies for distributed and multi-region setups, suggesting its applicability for institutions managing sensitive or regulated data.
Research and Academic Endorsement
Endorsed by Dr. Mohammad Tayebi from Simon Fraser University, the 25-page paper containing 17 figures underscores the intricacies of its architectural and mathematical foundations. It is classified under machine learning, artificial intelligence, and information theory categories on arXiv, emphasizing its contribution to the field.
Integrated Cyber Solutions Inc., doing business as Integrated Quantum Technologies, announced the publication of a white paper (the "Paper") by Mr. Jeremy Samuelson, EVP of AI and Innovation at IQT.
The Paper introduces VEIL™ (Vector Encoded Information Layer) and the VEILTM architecture, a privacy-preserving machine learning framework designed for use of sensitive data, and has been published on arXiv, the globally recognized open-access scientific research repository long hosted by Cornell University. The Paper has also been endorsed by Dr. Mohammad Tayebi, Assistant Professor of Professional Practice at Simon Fraser University.
Supervised machine learning
The Paper, titled "Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning," is now publicly available at arxiv website.
The Paper introduces Informationally Compressive Anonymization (ICA) and the VEILTM architecture, a framework to enable supervised machine learning on sensitive and regulated data while reducing exposure to raw inputs outside of trusted environments. The research contained in the Paper examines limitations associated with existing privacy-preserving machine learning approaches, including techniques such as homomorphic encryption and differential privacy, which may introduce computational overhead, increased latency, or reductions in predictive performance depending on implementation.
Multi-objective encoder
Pursuant to the Paper, the ICA approach embeds a supervised, multi-objective encoder within a trusted source environment to transform raw input into low-dimensional latent representations. Only these anonymised representations leave the trusted environment, ensuring that sensitive source data is not exposed during model training or inference. The Paper demonstrates that, under the assumptions analyzed, these representations are structurally non-invertible, meaning the original data cannot be constructed from the encoded outputs.
Unlike privacy methods that rely on cryptographic computation or stochastic noise injection, the Paper claims that VEILTM is designed to preserve predictive utility by explicitly aligning representation learning with downstream objectives. The Paper further notes that this approach uses architectural and informational constraints to protect data, with experimental results indicating predictive performance is maintained, or in some cases improved in the evaluated scenario, without the computational or scalability limitations associated with some existing privacy-preserving techniques.
Limiting reconstruction risk
The Paper presents a theoretical foundation for non-invertibility of encoded representations using topological and information-theoretic analysis. The Paper demonstrates that under idealised attacker assumptions, reconstruction of the original data is logically infeasible and that, in practical deployment, the probability of reconstruction approaches zero as attacker uncertainty increases. The analysis contained in the Paper further describes how dimensionality reduction and attacker uncertainty jointly contribute to limiting reconstruction risk.
The VEIL™ architecture described in the Paper establishes separation between source, training, and inference environments. The architecture described in the Paper defines boundaries designed to keep raw sensitive data within trusted environments while allowing encoded representations to be used in downstream machine learning workflows. The Paper also outlines deployment considerations for distributed environments and discusses how the architecture may be applied across multi-region deployments.
Machine learning workflows
The research in the Paper focuses on supervised machine learning workflows involving sensitive data inputs and provides a structured approach to encoding data prior to model training. The Paper describes how this architecture may be applicable to organizations with sensitive or regulated datasets, while minimizing data exposure in operational and governance considerations.
The Paper has been endorsed by Dr. Mohammad Tayebi, Assistant Professor of Professional Practice in the School of Computing Science at Simon Fraser University, whose research focuses on machine learning, cybersecurity, and AI Safety.
The Paper spans 25 pages and includes 17 figures detailing the architecture, mathematical foundations, and an experimental scenario described in the research. It is categorised under machine learning, artificial intelligence, and information theory on arXiv.
