EUVD-2021-19522

Malware in sbrugna...

EUVD-2010-0415

Malware in sbrugna...

EUVD-2007-3157

Malware in sbrugna...

EUVD-2011-2742

Malware in sbrugna...

EUVD-2017-7826

Malware in sbrugna...

PoS-CoPOR: Proof-Of-Stake Consensus Protocol with Native Onion Routing Providing Scalability and DoS-Resistance

Proof-of-Stake PoS consensus protocols often face a trade-off between performance and security. Protocols that pre-elect leaders for subsequent rounds are vulnerable to Denial-of-Service DoS attacks, which can disrupt the network and compromise liveness. In this work, we present PoS-CoPOR, a...

EUVD-2023-32740

Malicious code in bioql PyPI...

hackingtool

This is an all-in-one hacking tool for hackers, written in Python. The tool is designed to be run on Linux, Kali Linux, or Parrot OS. It provides a menu-driven interface for various hacking tasks, including information gathering, wireless attacks, SQL injection, phishing, web attacks,...

Linux Distros Unpatched Vulnerability : CVE-2007-1103

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - Tor does not verify a node's uptime and bandwidth advertisements, which allows remote attackers who operate a low resource node to make false claims of greater...

Linux Distros Unpatched Vulnerability : CVE-2022-21689

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - OnionShare is an open source tool that lets you securely and anonymously share files, host websites, and chat with friends using the Tor network. In affected...

Security Vulnerabilities in ICEBlock

The ICEBlock tool has vulnerabilities: The developer of ICEBlock, an iOS app for anonymously reporting sightings of US Immigration and Customs Enforcement ICE officials, promises that it "ensures user privacy by storing no personal data." But that claim has come under scrutiny. ICEBlock creator...

Efficient Unlearning with Privacy Guarantees

Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning ML models trained on them. Machine unlearning has emerged as a practical means to facilitate model forgetting of data...

Human-Centered Interactive Anonymization for Privacy-Preserving Machine Learning: a Case for Human-Guided K-Anonymity

Privacy-preserving machine learning ML seeks to balance data utility and privacy, especially as regulations like the GDPR mandate the anonymization of personal data for ML applications. Conventional anonymization approaches often reduce data utility due to indiscriminate generalization or...

Information-Theoretic Estimation of the Risk of Privacy Leaks

Recent work\citeLiu2016 has shown that dependencies between items in a dataset can lead to privacy leaks. We extend this concept to privacy-preserving transformations, considering a broader set of dependencies captured by correlation metrics. Specifically, we measure the correlation between the...

User Perceptions and Attitudes toward Untraceability in Messaging Platforms

Mainstream messaging platforms offer a variety of features designed to enhance user privacy, such as disappearing messages, password-protected chats, and end-to-end encryption E2EE, which primarily protect message contents. Beyond contents, the transmission of messages generates metadata that can...

A Hitchhiker'S Guide to Privacy-Preserving Cryptocurrencies: a Survey on Anonymity, Confidentiality, and Auditability

Cryptocurrencies and central bank digital currencies CBDCs are reshaping the monetary landscape, offering transparency and efficiency while raising critical concerns about user privacy and regulatory compliance. This survey provides a comprehensive and technically grounded overview of...

Anonymity-Washing

Anonymization is a foundational principle of data privacy regulation, yet its practical application remains riddled with ambiguity and inconsistency. This paper introduces the concept of anonymity-washing -- the misrepresentation of the anonymity level of sanitized'' personal data -- as a critica...

Towards Anonymous Neural Network Inference

We introduce funion, a system providing end-to-end sender-receiver unlinkability for neural network inference. By leveraging the Pigeonhole storage protocol and BACAP blinding-and-capability scheme from the Echomix anonymity system, funion inherits the provable security guarantees of modern...

Prink: $K_s$-Anonymization for Streaming Data in Apache Flink

In this paper, we present Prink, a novel and practically applicable concept and fully implemented prototype for ks-anonymizing data streams in real-world application architectures. Building upon the pre-existing, yet rudimentary CASTLE scheme, Prink for the first time introduces semantics-aware...

Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization'S Impact on ML Fairness

Machine learning ML algorithms are heavily based on the availability of training data, which, depending on the domain, often includes sensitive information about data providers. This raises critical privacy concerns. Anonymization techniques have emerged as a practical solution to address these...