Integrated Cyber Solutions Inc., doing business as Integrated Quantum Technologies ("IQT"), announced the publication of a white paper by Mr. Jeremy Samuelson, EVP of AI and Innovation at IQT. The Paper introduces VEIL™ (Vector Encoded Information Layer) and the VEIL™ 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.
The Paper, titled "Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning," is now publicly available at https://arxiv.org/pdf/2603.15842.
The following is a summary of certain information contained in the Paper. Readers are encouraged to review the Paper in full.
The Paper introduces Informationally Compressive Anonymization (ICA) and the VEIL™ 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.
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 anonymized 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 VEIL™ 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.
The Paper presents a theoretical foundation for non-invertibility of encoded representations using topological and information-theoretic analysis. The Paper demonstrates that under idealized 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.
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 categorized under machine learning, artificial intelligence, and information theory on arXiv.