The Summer Research Scholars (SRS) program supports students participating in collaborative research supervised by W&L faculty. The program aims to encourage the development of research techniques within a particular discipline, to promote the active acquisition of knowledge, and to stimulate student interest in inquiry.
Here are the 2024 Computer Science SRS students, their faculty supervisors and descriptions of their projects:
Professor Taha Khan:
Professor Khan had 2 SRS students this summer — Allison Badeaux ’28 and Nabil Youssef, ’26. The topic for their research was “Internet Censorship: A Self-Destructing Prophecy”.
The internet is a crucial medium for communication, information sharing, and freedom of expression. However, internet censorship poses significant challenges to these aspects. The study explores the multifaceted impacts of censorship on users’ behaviors, attitudes, and strategies to bypass restrictions.
Allison and Nabil’s research examined the effectiveness of censorship and its potential counterproductive effects. While censorship aims to control information flow and maintain social order, it often leads to unintended consequences like increased efforts to circumvent restrictions and the spread of alternative information channels. By analyzing user reactions, the study seeks to determine whether censorship achieves its goals or inadvertently promotes greater resistance and innovation in information dissemination.
Professor Simon Levy:
Professor Liz Matthews:
Professor William Tolley:
Professor Tolley had two projects with a total of four students this summer. The objectives, goals and direction for each project, along with the student research students are listed here.
This research endeavors to establish a framework for the secure dissemination of radio signals, ensuring integrity verification by external entities without disclosing the underlying raw data. The approach integrates advanced cryptographic constructs with robust signal feature extraction methodologies, employing fuzzy hashing and locality-sensitive hashing (LSH) within an error-tolerant paradigm.
The principal challenge addressed is the circumvention of legal constraints on signal sharing through the application of zero-knowledge proof analogs to continuous analog signals. By leveraging wavelet transform techniques, we aim to optimally decompose and isolate salient features of the signal, minimizing the impact of stochastic noise. These features are then encoded into a cryptographic hash, facilitating secure, non-invasive verification across different instances.
Ongoing research will focus on refining transformation techniques to enable the derivation of a unique signal fingerprint from its hash, advancing towards a framework where the hash itself encapsulates sufficient information-theoretic properties to characterize the original signal with high fidelity. This includes the potential to identify specific types of traffic, such as VPN traffic, thereby broadening the applicability of the methodology in network security and traffic analysis.
This project aims to adapt and apply advanced machine learning techniques, originally developed by Google for identifying vulnerable code and malicious executables in Android applications, to the detection of censorship and surveillance mechanisms. Given the impracticality of reverse engineering a vast number of apps, this approach leverages machine learning to automate the identification process.
The initial phase involves identifying and analyzing a set of Android apps known to contain censorship features. These apps will serve as a baseline for training machine learning models. The goal is to extrapolate from this initial dataset, enabling the model to scan and categorize thousands of other apps to detect similar censorship or surveillance-related behaviors. By refining these techniques, the project seeks to uncover hidden information control mechanisms within a broad spectrum of apps, facilitating more efficient and large-scale analysis of potentially censored content or surveillance activity.
