arXiv subjects

Kenneth Odoh

Publications and source records attributed to Kenneth Odoh.

6 recordsLinked to original sources

arXiv2025

An Architecture for Privacy-Preserving Telemetry Scheme

We present a privacy-preserving telemetry aggregation scheme. Our underlying frequency estimation routine works within the framework of differential privacy. The design philosophy follows a client-server architecture. Furthermore, the system uses a local differential privacy scheme where data gets randomized on the client before submitting the request to the resource server. This scheme allows for data analysis on de-identified data by carefully adding noise to prevent re-identification attacks, thereby facilitating public data release without compromising the identifiability of the individual record. This work further enhances privacy guarantees by leveraging Oblivious HTTP (OHTTP) to achieve increased privacy protection for data in transit that addresses pre-existing privacy vulnerabilities in raw HTTP. We provide an implementation that focuses on frequency estimation with a histogram of a known dictionary. Our resulting formulation based on OHTTP has provided stricter privacy safeguards when compared to trusting an organization to manually delete identifying information from the client's request in the ingestor as deployed in reference work~\cite{apple2017}. Code available at https://github.com/kenluck2001/miscellaneous/tree/master/src/Privacy-Preserving-Telemetry.

cs.CR↗

arXiv2025

Distributed Computing From First Principles

This book on Distributed Computing aims to benefit a diverse audience, ranging from aspiring engineers, and seasoned researchers, to a wide range of professionals. Driven by my passion for making the core concepts of distributed computing accessible, this work is a significant undertaking designed to empower individuals from all backgrounds to gain valuable insight. Have you ever wondered how a typical distributed system works under the hood? Are you looking for a pedagogical guide with complete implementations? In this work, we have implemented several foundational algorithms in Distributed Computing. Whether your expertise lies in the theoretical foundations or the practical applications of the principles of Distributed Systems, this book is for you.

cs.DC↗

arXiv2025

Information-theoretic Estimation of the Risk of Privacy Leaks

Recent work~\cite{Liu2016} has shown that dependencies between items in a dataset can lead to privacy leaks. We extend this concept to privacy-preserving transformations, considering a broader set of dependencies captured by correlation metrics. Specifically, we measure the correlation between the original data and their noisy responses from a randomizer as an indicator of potential privacy breaches. This paper aims to leverage information-theoretic measures, such as the Maximal Information Coefficient (MIC), to estimate privacy leaks and derive novel, computationally efficient privacy leak estimators. We extend the $\rho_1$-to-$\rho_2$ formulation~\cite{Evfimievski2003} to incorporate entropy, mutual information, and the degree of anonymity for a more comprehensive measure of privacy risk. Our proposed hybrid metric can identify correlation dependencies between attributes in the dataset, serving as a proxy for privacy leak vulnerabilities. This metric provides a computationally efficient worst-case measure of privacy loss, utilizing the inherent characteristics of the data to prevent privacy breaches.

cs.CR↗

arXiv2024

Connect the dots: Dataset Condensation, Differential Privacy, and Adversarial Uncertainty

Our work focuses on understanding the underpinning mechanism of dataset condensation by drawing connections with ($\epsilon$, $\delta$)-differential privacy where the optimal noise, $\epsilon$, is chosen by adversarial uncertainty \cite{Grining2017}. We can answer the question about the inner workings of the dataset condensation procedure. Previous work \cite{dong2022} proved the link between dataset condensation (DC) and ($\epsilon$, $\delta$)-differential privacy. However, it is unclear from existing works on ablating DC to obtain a lower-bound estimate of $\epsilon$ that will suffice for creating high-fidelity synthetic data. We suggest that adversarial uncertainty is the most appropriate method to achieve an optimal noise level, $\epsilon$. As part of the internal dynamics of dataset condensation, we adopt a satisfactory scheme for noise estimation that guarantees high-fidelity data while providing privacy.

cs.CR↗

arXiv2023

Tortoise: An Authenticated Encryption Scheme

Given the open nature of the Internet, there is a need for authentication schemes to address inherent trust issues. We present Tortoise, an experimental nonce-based authenticated encryption scheme modeled on the Synthetic Counter-in-Tweak. This paper demonstrates a generalizable plug-and-play framework for converting block cipher into Authenticated Encryption with Associated Data. As part of this work, we utilized an XOR procedure for constructing a generic tweakable cipher. Finally, we support two modes: nonce-respecting and nonce-misuse-resistant. Source code available at https://github.com/kenluck2001/cipherResearch/tree/main/src/tortoise.

cs.CR↗

arXiv2022

Real-time Anomaly Detection for Multivariate Data Streams

We present a real-time multivariate anomaly detection algorithm for data streams based on the Probabilistic Exponentially Weighted Moving Average (PEWMA). Our formulation is resilient to (abrupt transient, abrupt distributional, and gradual distributional) shifts in the data. The novel anomaly detection routines utilize an incremental online algorithm to handle streams. Furthermore, our proposed anomaly detection algorithm works in an unsupervised manner eliminating the need for labeled examples. Our algorithm performs well and is resilient in the face of concept drifts.

cs.AI↗