CVE-2024-32969

vantage6 is an open-source infrastructure for privacy preserving analysis. Collaboration administrators can add extra organizations to their collaboration that can extend their influence. For example, organizations that they include can then create new users for which they know the passwords, and...

CVE-2023-28635

vantage6 is privacy preserving federated learning infrastructure. Prior to version 4.0.0, malicious users may try to get access to resources they are not allowed to see, by creating resources with integers as names. One example where this is a risk, is when users define which users are allowed to...

CVE-2023-41881

vantage6 is privacy preserving federated learning infrastructure. When a collaboration is deleted, the linked resources such as tasks from that collaboration should be deleted. This is partly to manage data properly, but also to prevent a potential but unlikely side-effect that affects versions...

CVE-2023-41882

vantage6 is privacy preserving federated learning infrastructure. The endpoint /api/collaboration/id/task is used to collect all tasks from a certain collaboration. To get such tasks, a user should have permission to view the collaboration and to view the tasks in it. However, prior to version...

CVE-2020-8276

The implementation of Brave Desktop's privacy-preserving analytics system P3A between 1.1 and 1.18.35 logged the timestamp of when the user last opened an incognito window, including Tor windows. The intended behavior was to log the timestamp for incognito windows excluding Tor windows. Note that...

Pura: an Efficient Privacy-Preserving Solution for Face Recognition

Face recognition is an effective technology for identifying a target person by facial images. However, sensitive facial images raises privacy concerns. Although privacy-preserving face recognition is one of potential solutions, this solution neither fully addresses the privacy concerns nor is...

A Survey on Secure Machine Learning

In this survey, we will explore the interaction between secure multiparty computation and the area of machine learning. Recent advances in secure multiparty computation MPC have significantly improved its applicability in the realm of machine learning ML, offering robust solutions for...

Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT

Industrial Internet of Things IIoT systems have become integral to smart manufacturing, yet their growing connectivity has also exposed them to significant cybersecurity threats. Traditional intrusion detection systems IDS often rely on centralized architectures that raise concerns over data...

Proof-Of-Social-Capital: Privacy-Preserving Consensus Protocol Replacing Stake for Social Capital

Consensus protocols used today in blockchains often rely on computational power or financial stakes - scarce resources. We propose a novel protocol using social capital - trust and influence from social interactions - as a non-transferable staking mechanism to ensure fairness and decentralization...

On Membership Inference Attacks in Knowledge Distillation

Nowadays, Large Language Models LLMs are trained on huge datasets, some including sensitive information. This poses a serious privacy concern because privacy attacks such as Membership Inference Attacks MIAs may detect this sensitive information. While knowledge distillation compresses LLMs into...

Improved Algorithms for Differentially Private Language Model Alignment

Language model alignment is crucial for ensuring that large language models LLMs align with human preferences, yet it often involves sensitive user data, raising significant privacy concerns. While prior work has integrated differential privacy DP with alignment techniques, their performance...

Source Anonymity for Private Random Walk Decentralized Learning

This paper considers random walk-based decentralized learning, where at each iteration of the learning process, one user updates the model and sends it to a randomly chosen neighbor until a convergence criterion is met. Preserving data privacy is a central concern and open problem in decentralize...

Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning

Functional encryption FE has recently attracted interest in privacy-preserving machine learning PPML for its unique ability to compute specific functions on encrypted data. A related line of work focuses on noisy FE, which ensures differential privacy in the output while keeping the data encrypte...

Optimal Regret of Bernoulli Bandits under Global Differential Privacy

As sequential learning algorithms are increasingly applied to real life, ensuring data privacy while maintaining their utilities emerges as a timely question. In this context, regret minimisation in stochastic bandits under $ε$-global Differential Privacy DP has been widely studied. Unlike bandit...

Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: a Scoping Review

Explainable Artificial Intelligence XAI has emerged as a pillar of Trustworthy AI and aims to bring transparency in complex models that are opaque by nature. Despite the benefits of incorporating explanations in models, an urgent need is found in addressing the privacy concerns of providing this...

Encrypted Federated Search Using Homomorphic Encryption

The sharing of information between agencies is effective in dealing with cross-jurisdictional criminal activities; however, such sharing is often restricted due to concerns about data privacy, ownership, and compliance. Towards this end, this work has introduced a privacy-preserving federated...

Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of...

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning

The widespread adoption of Artificial Intelligence AI has been driven by significant advances in intelligent system research. However, this progress has raised concerns about data privacy, leading to a growing awareness of the need for privacy-preserving AI. In response, there has been a seismic...

Preserving Privacy and Utility in LLM-Based Product Recommendations

Large Language Model LLM-based recommendation systems leverage powerful language models to generate personalized suggestions by processing user interactions and preferences. Unlike traditional recommendation systems that rely on structured data and collaborative filtering, LLM-based models proces...

CryptoUNets: Applying Convolutional Networks to Encrypted Data for Biomedical Image Segmentation

In this manuscript, we demonstrate the feasibility of a privacy-preserving U-Net deep learning inference framework, namely, homomorphic encryption-based U-Net inference. That is, U-Net inference can be performed solely using homomorphic encryption techniques. To our knowledge, this is the first...