insikt - Computer Security and Privacy - # Differentially Private Contact Tracing with Neural Augmentation

Differentially Private Neural Augmentation for Contact Tracing

Q: How can the privacy composition analysis be extended to account for groups of attackers repeatedly contacting the same individual?

In the context of contact tracing, where groups of attackers may repeatedly contact the same individual, the privacy composition analysis can be extended by considering the cumulative impact of these repeated contacts on the individual's privacy. One approach could be to analyze the sensitivity of the algorithm not just based on individual messages per user but also taking into account the collective effect of multiple messages from the same group of attackers. This would involve assessing the potential information leakage and privacy risks that arise from repeated interactions with the same set of attackers. To address this, the analysis could incorporate a mechanism to quantify the impact of repeated contacts on the overall privacy of the individual. This could involve defining a metric to measure the cumulative sensitivity of the algorithm to repeated messages from the same group of attackers. By considering the collective influence of these repeated interactions, the privacy composition analysis can provide a more comprehensive understanding of the privacy implications in scenarios where individuals are targeted by multiple attackers. Furthermore, the analysis could explore strategies to mitigate the privacy risks associated with repeated contacts. This may involve implementing additional safeguards or privacy-preserving measures to protect individuals from potential privacy breaches resulting from persistent targeting by groups of attackers. By incorporating these considerations into the privacy composition analysis, researchers can enhance the robustness of the algorithm and ensure that privacy is effectively maintained in the face of repeated contact scenarios.

Q: What potential biases might differential privacy introduce in the contact tracing context, and how can they be mitigated?

Differential privacy in the context of contact tracing may introduce potential biases that could impact the effectiveness and fairness of the algorithm. One potential bias is the differential impact on different demographic groups, leading to disparities in the accuracy of predictions and the allocation of resources. For example, differential privacy measures may inadvertently favor certain groups over others, resulting in unequal treatment and outcomes. To mitigate these biases, it is essential to conduct thorough bias assessments and fairness evaluations throughout the development and deployment of the contact tracing algorithm. This includes analyzing the data inputs, model outputs, and decision-making processes to identify and address any biases that may arise. Additionally, researchers can implement bias mitigation techniques such as fairness-aware training, bias detection algorithms, and model interpretability tools to ensure that differential privacy measures do not exacerbate existing biases or introduce new ones. Furthermore, incorporating diverse perspectives and expertise in the design and evaluation of the contact tracing algorithm can help identify and mitigate biases effectively. By engaging with stakeholders from different backgrounds and communities, researchers can gain valuable insights into potential biases and develop strategies to promote fairness and equity in the implementation of differential privacy measures.

Q: How can the DNA approach be adapted to work in a federated learning setting, where the neural augmentation module is trained across multiple devices while preserving privacy?

Adapting the DNA approach to work in a federated learning setting involves addressing the challenges of training the neural augmentation module across multiple devices while preserving privacy and maintaining the integrity of the model. One key consideration is ensuring that differential privacy guarantees are maintained throughout the federated learning process to protect the privacy of individual data contributors. To achieve this, researchers can implement privacy-preserving techniques such as federated differential privacy, which combines the principles of federated learning and differential privacy to enable collaborative model training while preserving the privacy of individual data. This involves aggregating model updates in a privacy-preserving manner, such as through secure aggregation protocols, to prevent the leakage of sensitive information during the training process. Additionally, researchers can incorporate secure and encrypted communication protocols to ensure that data transmission between devices is protected from unauthorized access or interception. By leveraging cryptographic techniques such as homomorphic encryption and secure multi-party computation, the privacy of data exchanged during federated learning can be safeguarded while enabling collaborative model training across distributed devices. Furthermore, establishing clear governance frameworks and data sharing agreements among participants in the federated learning setting can help ensure transparency, accountability, and compliance with privacy regulations. By implementing robust privacy and security measures, the DNA approach can be effectively adapted to work in a federated learning environment, enabling collaborative model training while upholding privacy standards and data protection principles.

Centrala begrepp

A novel algorithm for statistical contact tracing using neural augmentation that maintains differential privacy against a recently identified attack scenario.

Sammanfattning

The paper proposes a novel algorithm called Differentially private Neural Augmentation (DNA) for statistical contact tracing. The key contributions are:

Augmenting the statistical inference in contact tracing with a learned neural network, while ensuring the neural augmentation satisfies differential privacy. This bridges the promising field of neural augmentation with differential privacy.
Identifying a novel hierarchy of privacy in contact tracing, providing a theoretical proof of differential privacy at the most general level of the hierarchy.
Evaluating the methods on a widely used simulator, showing that even in challenging situations like noisy tests or agents not following the protocol, the DNA method significantly reduces the peak infection rate compared to prior differentially private methods.

The statistical model for contact tracing is based on a Markov chain of disease states (Susceptible, Exposed, Infected, Recovered) and noisy observations from COVID-19 tests. The authors augment the statistical inference with a neural network that learns patterns not captured by the statistical model, while bounding the sensitivity of the neural network to ensure differential privacy.

The analysis identifies three levels of privacy composition in contact tracing - per message, per day, and across multiple days. The authors prove that the sensitivity of the Factorized Neighbors (FN) algorithm is bounded, which motivates the neural augmentation module to have a similar bound.

Experiments on a COVID-19 simulator show that at the crucial setting of ε=1 differential privacy, the DNA method achieves significantly lower peak infection rates compared to prior differentially private and non-private methods. The method also demonstrates robustness to challenges like noisy tests and non-compliant agents.

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Statistik

The peak infection rate (PIR), which is the largest fraction of simultaneously infected individuals, is a key metric used to evaluate the methods.

Citat

"We substantially improve the privacy guarantees of the current state of the art in decentralized contact tracing. Whereas previous work was based on statistical inference only, we augment the inference with a learned neural network and ensure that this neural augmentation satisfies differential privacy."
"Both methods are tested on a widely used simulator. Even in the challenging situation of noisy tests, or agents not following the protocol, our method significantly reduces the number of simultaneously infected individuals, which is a key marker for pandemic mitigation."

Viktiga insikter från

DNA: Differentially private Neural Augmentation for contact tracing

by Rob Romijnde... på arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13381.pdf

DNA: Differentially private Neural Augmentation for contact tracing

Djupare frågor

How can the privacy composition analysis be extended to account for groups of attackers repeatedly contacting the same individual?

In the context of contact tracing, where groups of attackers may repeatedly contact the same individual, the privacy composition analysis can be extended by considering the cumulative impact of these repeated contacts on the individual's privacy. One approach could be to analyze the sensitivity of the algorithm not just based on individual messages per user but also taking into account the collective effect of multiple messages from the same group of attackers. This would involve assessing the potential information leakage and privacy risks that arise from repeated interactions with the same set of attackers.
To address this, the analysis could incorporate a mechanism to quantify the impact of repeated contacts on the overall privacy of the individual. This could involve defining a metric to measure the cumulative sensitivity of the algorithm to repeated messages from the same group of attackers. By considering the collective influence of these repeated interactions, the privacy composition analysis can provide a more comprehensive understanding of the privacy implications in scenarios where individuals are targeted by multiple attackers.
Furthermore, the analysis could explore strategies to mitigate the privacy risks associated with repeated contacts. This may involve implementing additional safeguards or privacy-preserving measures to protect individuals from potential privacy breaches resulting from persistent targeting by groups of attackers. By incorporating these considerations into the privacy composition analysis, researchers can enhance the robustness of the algorithm and ensure that privacy is effectively maintained in the face of repeated contact scenarios.

What potential biases might differential privacy introduce in the contact tracing context, and how can they be mitigated?

Differential privacy in the context of contact tracing may introduce potential biases that could impact the effectiveness and fairness of the algorithm. One potential bias is the differential impact on different demographic groups, leading to disparities in the accuracy of predictions and the allocation of resources. For example, differential privacy measures may inadvertently favor certain groups over others, resulting in unequal treatment and outcomes.
To mitigate these biases, it is essential to conduct thorough bias assessments and fairness evaluations throughout the development and deployment of the contact tracing algorithm. This includes analyzing the data inputs, model outputs, and decision-making processes to identify and address any biases that may arise. Additionally, researchers can implement bias mitigation techniques such as fairness-aware training, bias detection algorithms, and model interpretability tools to ensure that differential privacy measures do not exacerbate existing biases or introduce new ones.
Furthermore, incorporating diverse perspectives and expertise in the design and evaluation of the contact tracing algorithm can help identify and mitigate biases effectively. By engaging with stakeholders from different backgrounds and communities, researchers can gain valuable insights into potential biases and develop strategies to promote fairness and equity in the implementation of differential privacy measures.

How can the DNA approach be adapted to work in a federated learning setting, where the neural augmentation module is trained across multiple devices while preserving privacy?

Adapting the DNA approach to work in a federated learning setting involves addressing the challenges of training the neural augmentation module across multiple devices while preserving privacy and maintaining the integrity of the model. One key consideration is ensuring that differential privacy guarantees are maintained throughout the federated learning process to protect the privacy of individual data contributors.
To achieve this, researchers can implement privacy-preserving techniques such as federated differential privacy, which combines the principles of federated learning and differential privacy to enable collaborative model training while preserving the privacy of individual data. This involves aggregating model updates in a privacy-preserving manner, such as through secure aggregation protocols, to prevent the leakage of sensitive information during the training process.
Additionally, researchers can incorporate secure and encrypted communication protocols to ensure that data transmission between devices is protected from unauthorized access or interception. By leveraging cryptographic techniques such as homomorphic encryption and secure multi-party computation, the privacy of data exchanged during federated learning can be safeguarded while enabling collaborative model training across distributed devices.
Furthermore, establishing clear governance frameworks and data sharing agreements among participants in the federated learning setting can help ensure transparency, accountability, and compliance with privacy regulations. By implementing robust privacy and security measures, the DNA approach can be effectively adapted to work in a federated learning environment, enabling collaborative model training while upholding privacy standards and data protection principles.