arXiv subjects

Kyle Mylonakis

Publications and source records attributed to Kyle Mylonakis.

3 recordsLinked to original sources

arXiv2025

Learning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform

The high cost of ownership of AI compute infrastructure and challenges of robust serving of large language models (LLMs) has led to a surge in managed Model-as-a-service deployments. Even when enterprises choose on-premises deployments, the compute infrastructure is typically shared across many teams in order to maximize the return on investment. In both scenarios the deployed models operate only on plaintext data, and so enterprise data owners must allow their data to appear in plaintext on a shared or multi-tenant compute infrastructure. This results in data owners with private or sensitive data being hesitant or restricted in what data they use with these types of deployments. In this work we introduce the Stained Glass Transform, a learned, stochastic, and sequence dependent transformation of the word embeddings of an LLM which information theoretically provides privacy to the input of the LLM while preserving the utility of model. We theoretically connect a particular class of Stained Glass Transforms to the theory of mutual information of Gaussian Mixture Models. We then calculate a-postiori privacy estimates, based on mutual information, and verify the privacy and utility of instances of transformed embeddings through token level metrics of privacy and standard LLM performance benchmarks.

cs.LG↗

arXiv2025

BeamClean: Language Aware Embedding Reconstruction

In this work, we consider an inversion attack on the obfuscated input embeddings sent to a language model on a server, where the adversary has no access to the language model or the obfuscation mechanism and sees only the obfuscated embeddings along with the model's embedding table. We propose BeamClean, an inversion attack that jointly estimates the noise parameters and decodes token sequences by integrating a language-model prior. Against Laplacian and Gaussian obfuscation mechanisms, BeamClean always surpasses naive distance-based attacks. This work highlights the necessity for and robustness of more advanced learned, input-dependent methods.

cs.CR↗

arXiv2018

Deep Learning Seismic Substructure Detection using the Frozen Gaussian Approximation

We propose a deep learning algorithm for seismic interface and pocket detection with neural networks trained by synthetic high-frequency displacement data efficiently generated by the frozen Gaussian approximation (FGA). In seismic imaging high-frequency data is advantageous since it can provide high resolution of substructures. However, generation of sufficient synthetic high-frequency data sets for training neural networks is computationally challenging. This bottleneck is overcome by a highly scalable computational platform built upon the FGA, which comes from the semiclassical theory and approximates the wavefields by a sum of fixed-width (frozen) Gaussian wave packets. Data is generated from a forward simulation of the elastic wave equation using the FGA. This data contains accurate traveltime information (from the ray path) but not exact amplitude information (with asymptotic errors not shrinking to zero even at extremely fine numerical resolution). Using this data we build convolutional neural network models using an open source API, GeoSeg, developed using Keras and Tensorflow. On a simple model, networks, despite only being trained on FGA data, can detect an interface with a high success rate from displacement data generated by the spectral element method. Benchmark tests are done for P-waves (acoustic) and P- and S-waves (elastic) generated using the FGA and a spectral element method. Further, results with a high accuracy are shown for more complicated geometries including a three layered model, and a 2D-pocket model where the neural networks trained by both clean and noisy data.

physics.geo-ph↗