Differentially Private Federated $K$-Means Clustering with Server-Side Data

Clustering is a cornerstone of data analysis that is particularly suited to identifying coherent subgroups or substructures in unlabeled data, as are generated continuously in large amounts these days. However, in many cases traditional clustering methods are not applicable, because data are...

First-Spammed, First-Served: MEV Extraction on Fast-Finality Blockchains

This research analyzes the economics of spam-based arbitrage strategies on fast-finality blockchains. We begin by theoretically demonstrating that, splitting a profitable MEV opportunity into multiple small transactions is the optimal strategy for CEX-DEX arbitrageurs. We then empirically validat...

Differentially Private Explanations for Clusters

The dire need to protect sensitive data has led to various flavors of privacy definitions. Among these, Differential privacy DP is considered one of the most rigorous and secure notions of privacy, enabling data analysis while preserving the privacy of data contributors. One of the fundamental...

Urania: Differentially Private Insights into AI Use

We introduce $Urania$, a novel framework for generating insights about LLM chatbot interactions with rigorous differential privacy DP guarantees. The framework employs a private clustering mechanism and innovative keyword extraction methods, including frequency-based, TF-IDF-based, and LLM-guided...

Clustering and Median Aggregation Improve Differentially Private Inference

Differentially private DP language model inference is an approach for generating private synthetic text. A sensitive input example is used to prompt an off-the-shelf large language model LLM to produce a similar example. Multiple examples can be aggregated together to formally satisfy the DP...

PackHero: a Scalable Graph-Based Approach for Efficient Packer Identification

Anti-analysis techniques, particularly packing, challenge malware analysts, making packer identification fundamental. Existing packer identifiers have significant limitations: signature-based methods lack flexibility and struggle against dynamic evasion, while Machine Learning approaches require...

Privacy-Preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models

Prompt learning is a crucial technique for adapting pre-trained multimodal language models MLLMs to user tasks. Federated prompt personalization FPP is further developed to address data heterogeneity and local overfitting, however, it exposes personalized prompts - valuable intellectual assets - ...

Poison in the Well: Feature Embedding Disruption in Backdoor Attacks

Backdoor attacks embed malicious triggers into training data, enabling attackers to manipulate neural network behavior during inference while maintaining high accuracy on benign inputs. However, existing backdoor attacks face limitations manifesting in excessive reliance on training data, poor...

CVE-2024-46942

In OpenDaylight Model-Driven Service Abstraction Layer MD-SAL through 13.0.1, a controller with a follower role can configure flow entries in an OpenDaylight clustering deployment...

CVE-2024-53257

Vitess is a database clustering system for horizontal scaling of MySQL. The /debug/querylogz and /debug/env pages for vtgate and vttablet do not properly escape user input. The result is that queries executed by Vitess can write HTML into the monitoring page at will. These pages are rendered usin...

CVE-2023-45177

IBM MQ 9.0 LTS, 9.1 LTS, 9.2 LTS, 9.3 LTS and 9.3 CD is vulnerable to a denial-of-service attack due to an error within the MQ clustering logic. IBM X-Force ID: 268066...

Real-Time Detection of Insider Threats Using Behavioral Analytics and Deep Evidential Clustering

Insider threats represent one of the most critical challenges in modern cybersecurity. These threats arise from individuals within an organization who misuse their legitimate access to harm the organization's assets, data, or operations. Traditional security mechanisms, primarily designed for...

Privacy-Preserving Socialized Recommendation Based on Multi-View Clustering in a Cloud Environment

Recommendation as a service has improved the quality of our lives and plays a significant role in variant aspects. However, the preference of users may reveal some sensitive information, so that the protection of privacy is required. In this paper, we propose a privacy-preserving, socialized,...

DELL PowerScale OneFS Competitive Conditions Vulnerability

DELL PowerScale OneFS is Dell's horizontally scalable clustered file system designed to manage unstructured data and support enterprise-class storage capabilities. A competitive condition vulnerability exists in DELL PowerScale OneFS, which can be exploited by attackers to cause a denial of servi...

On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models

We investigate privacy-preserving spectral clustering for community detection within stochastic block models SBMs. Specifically, we focus on edge differential privacy DP and propose private algorithms for community recovery. Our work explores the fundamental trade-offs between the privacy budget...

An Approach for Handling Missing Attribute Values in Attribute-Based Access Control Policy Mining

Attribute-Based Access Control ABAC enables highly expressive and flexible access decisions by considering a wide range of contextual attributes. ABAC policies use logical expressions that combine these attributes, allowing for precise and context-aware control. Algorithms that mine ABAC policies...

Enhancing Leakage Attacks on Searchable Symmetric Encryption Using LLM-Based Synthetic Data Generation

Searchable Symmetric Encryption SSE enables efficient search capabilities over encrypted data, allowing users to maintain privacy while utilizing cloud storage. However, SSE schemes are vulnerable to leakage attacks that exploit access patterns, search frequency, and volume information. Existing...

On the Price of Differential Privacy for Hierarchical Clustering

Hierarchical clustering is a fundamental unsupervised machine learning task with the aim of organizing data into a hierarchy of clusters. Many applications of hierarchical clustering involve sensitive user information, therefore motivating recent studies on differentially private hierarchical...

Topological Analysis of Mixer Activities in the Bitcoin Network

Cryptocurrency users increasingly rely on obfuscation techniques such as mixers, swappers, and decentralised or no-KYC exchanges to protect their anonymity. However, at the same time, these services are exploited by criminals to conceal and launder illicit funds. Among obfuscation services, mixer...

Clustering and Analysis of User Behaviour in Blockchain: a Case Study of Planet IX

Decentralised applications dApps that run on public blockchains have the benefit of trustworthiness and transparency as every activity that happens on the blockchain can be publicly traced through the transaction data. However, this introduces a potential privacy problem as this data can be track...