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Edwin P. Kayang

Publications and source records attributed to Edwin P. Kayang.

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DALC-CT: Dynamic Analysis of Low-Level Code Traces for Constant-Time Verification

Timing side-channel attacks exploit variations in program execution time to recover sensitive information. Cryptographic implementations are especially vulnerable to these attacks, since even small timing differences in operations such as modular exponentiation or key comparisons can be exploited to extract highly sensitive information, such as secret keys. To mitigate this threat, implementations of programs that handle sensitive information are often expected to adhere to constant-time principles, ensuring that execution behavior does not depend on secret inputs. However, validating the constant-time property of programs remains a major challenge in cryptography development. Formal method approaches to verify constant-time implementations rely on abstractions that often fail to capture real execution behavior, while timing-based measurement techniques are highly sensitive to noise from other programs and even hardware environments. In this work, we propose a novel approach for verifying constant-time programs based on dynamic analysis of low-level execution traces. Our method measures instruction sequences across multiple input values for any given binary and targeted function. Any variations in the instruction mix distribution for any given pair of traces indicate a deviation from the constant-time principle and behavior. We developed an open-source tool called DALC-CT, for the constant-time verification of programs using this approach. We evaluated it on a set of well-known constant-time and non-constant-time examples, achieving a perfect detection of issues. Our results demonstrate that analyzing the logical execution of programs via instruction trace comparisons provides a lightweight and reliable way to verify the constant-time property of programs.

cs.CR

Prismo: A Decision Support System for Privacy-Preserving ML Framework Selection

Machine learning has become a crucial part of our lives, with applications spanning nearly every aspect of our daily activities. However, using personal information in machine learning applications has sparked significant security and privacy concerns about user data. To address these challenges, different privacy-preserving machine learning (PPML) frameworks have been developed to protect sensitive information in machine learning applications. These frameworks generally attempt to balance design trade-offs such as computational efficiency, communication overhead, security guarantees, and scalability. Despite the advancements, selecting the optimal framework and parameters for specific deployment scenarios remains a complex and critical challenge for privacy and security application developers. We present Prismo, an open-source recommendation system designed to aid in selecting optimal parameters and frameworks for different PPML application scenarios. Prismo enables users to explore a comprehensive space of PPML frameworks through various properties based on user-defined objectives. It supports automated filtering of suitable candidate frameworks by considering parameters such as the number of parties in multi-party computation or federated learning and computation cost constraints in homomorphic encryption. Prismo models every use case into a Linear Integer Programming optimization problem, ensuring tailored solutions are recommended for each scenario. We evaluate Prismo's effectiveness through multiple use cases, demonstrating its ability to deliver best-fit solutions in different deployment scenarios.

cs.CR