arXiv subjects

Micha Moffie

Publications and source records attributed to Micha Moffie.

3 recordsLinked to original sources

arXiv2025

Cryptoscope: Analyzing cryptographic usages in modern software

The advent of quantum computing poses a significant challenge as it has the potential to break certain cryptographic algorithms, necessitating a proactive approach to identify and modernize cryptographic code. Identifying these cryptographic elements in existing code is only the first step. It is crucial not only to identify quantum vulnerable algorithms but also to detect vulnerabilities and incorrect crypto usages, to prioritize, report, monitor as well as remediate and modernize code bases. A U.S. government memorandum require agencies to begin their transition to PQC (Post Quantum Cryptograpy) by conducting a prioritized inventory of cryptographic systems including software and hardware systems. In this paper we describe our code scanning tool - Cryptoscope - which leverages cryptographic domain knowledge as well as compiler techniques to statically parse and analyze source code. By analyzing control and data flow the tool is able to build an extendable and querriable inventory of cryptography. Cryptoscope goes beyond identifying disconnected cryptographic APIs and instead provides the user with an inventory of cryptographic assets - containing comprehensive views of the cryptographic operations implemented. We show that for more than 92% of our test cases, these views include the cryptographic operation itself, APIs, as well as the related material such as keys, nonces, random sources etc. Lastly, building on top of this inventory, our tool is able to detect and report all the cryptographic related weaknesses and vulnerabilities (11 out of 15) in CamBench - achieving state-of-the-art performance.

cs.CR↗

arXiv2021

Anonymizing Machine Learning Models

There is a known tension between the need to analyze personal data to drive business and privacy concerns. Many data protection regulations, including the EU General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA), set out strict restrictions and obligations on the collection and processing of personal data. Moreover, machine learning models themselves can be used to derive personal information, as demonstrated by recent membership and attribute inference attacks. Anonymized data, however, is exempt from the obligations set out in these regulations. It is therefore desirable to be able to create models that are anonymized, thus also exempting them from those obligations, in addition to providing better protection against attacks. Learning on anonymized data typically results in significant degradation in accuracy. In this work, we propose a method that is able to achieve better model accuracy by using the knowledge encoded within the trained model, and guiding our anonymization process to minimize the impact on the model's accuracy, a process we call accuracy-guided anonymization. We demonstrate that by focusing on the model's accuracy rather than generic information loss measures, our method outperforms state of the art k-anonymity methods in terms of the achieved utility, in particular with high values of k and large numbers of quasi-identifiers. We also demonstrate that our approach has a similar, and sometimes even better ability to prevent membership inference attacks as approaches based on differential privacy, while averting some of their drawbacks such as complexity, performance overhead and model-specific implementations. This makes model-guided anonymization a legitimate substitute for such methods and a practical approach to creating privacy-preserving models.

cs.CR↗

arXiv2020

Data Minimization for GDPR Compliance in Machine Learning Models

The EU General Data Protection Regulation (GDPR) mandates the principle of data minimization, which requires that only data necessary to fulfill a certain purpose be collected. However, it can often be difficult to determine the minimal amount of data required, especially in complex machine learning models such as neural networks. We present a first-of-a-kind method to reduce the amount of personal data needed to perform predictions with a machine learning model, by removing or generalizing some of the input features. Our method makes use of the knowledge encoded within the model to produce a generalization that has little to no impact on its accuracy. This enables the creators and users of machine learning models to acheive data minimization, in a provable manner.

cs.LG↗