arXiv subjects

Co-Pierre Georg

Publications and source records attributed to Co-Pierre Georg.

4 recordsLinked to original sources

arXiv2024

Vulnerability Webs: Systemic Risk in Software Networks

Software development relies on code reuse to minimize costs, creating vulnerability risks through dependencies with substantial economic impact, as seen in the Crowdstrike and HeartBleed incidents. We analyze 52,897 dependencies across 16,102 Python repositories using a strategic network formation model incorporating observable and unobservable heterogeneity. Through variational approximation of conditional distributions, we demonstrate that dependency creation generates negative externalities. Vulnerability propagation, modeled as a contagion process, shows that popular protection heuristics are ineffective. AI-assisted coding, on the other hand, offers an effective alternative by enabling dependency replacement with in-house code.

econ.EM↗

arXiv2018

A privacy-preserving system for data ownership using blockchain and distributed databases

Blockchain has the potential to revolutionize the way we store, use, and process data. Information on most blockchains can be viewed by every node hosting the blockchain, which means that most blockchains cannot handle private data. Decentralized databases exist that guarantee privacy by encrypting user data with the user's private key, but this prevents easy data sharing. However, in many real world applications, from student data to medical records, it is desirable that user data is anonymously searchable. In this paper we present a novel system that gives users ownership over their data while at the same time enabling them to make their data searchable within previously agreed upon limits. Our system implements a strong notion of ownership using a self-sovereign identity system and a weak notion of ownership using multiple centralized databases together with a blockchain and a tumbling process. We discuss applications of our methods to university's student records and medical data.

cs.CR↗

arXiv2017

Fake News in Social Networks

We propose multi-agent reinforcement learning as a new method for modeling fake news in social networks. This method allows us to model human behavior in social networks both in unaccustomed populations and in populations that have adapted to the presence of fake news. In particular the latter is challenging for existing methods. We find that a fake-news attack is more effective if it targets highly connected people and people with weaker private information. Attacks are more effective when the disinformation is spread across several agents than when the disinformation is concentrated with more intensity on fewer agents. Furthermore, fake news spread less well in balanced networks than in clustered networks. We test a part of our findings in a human-subject experiment. The experimental evidence provides support for the predictions from the model, suggesting that the model is suitable to analyze the spread of fake news in social networks.

cs.AI↗

arXiv2014

Contagious Synchronization and Endogenous Network Formation in Financial Networks

When banks choose similar investment strategies the financial system becomes vulnerable to common shocks. We model a simple financial system in which banks decide about their investment strategy based on a private belief about the state of the world and a social belief formed from observing the actions of peers. Observing a larger group of peers conveys more information and thus leads to a stronger social belief. Extending the standard model of Bayesian updating in social networks, we show that the probability that banks synchronize their investment strategy on a state non-matching action critically depends on the weighting between private and social belief. This effect is alleviated when banks choose their peers endogenously in a network formation process, internalizing the externalities arising from social learning.

econ.GN↗