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View article: MoPE: A Mixture of Password Experts for Improving Password Guessing
MoPE: A Mixture of Password Experts for Improving Password Guessing Open
Textual passwords remain a predominant authentication mechanism in web security. To evaluate their strength, existing research has proposed several data-driven models across various scenarios. However, these models generally treat password…
View article: IDCloak: A Practical Secure Multi-party Dataset Join Framework for Vertical Privacy-preserving Machine Learning
IDCloak: A Practical Secure Multi-party Dataset Join Framework for Vertical Privacy-preserving Machine Learning Open
Vertical privacy-preserving machine learning (vPPML) enables multiple parties to train models on their vertically distributed datasets while keeping datasets private. In vPPML, it is critical to perform the secure dataset join, which align…
View article: PII-Bench: Evaluating Query-Aware Privacy Protection Systems
PII-Bench: Evaluating Query-Aware Privacy Protection Systems Open
The widespread adoption of Large Language Models (LLMs) has raised significant privacy concerns regarding the exposure of personally identifiable information (PII) in user prompts. To address this challenge, we propose a query-unrelated PI…
View article: HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning
HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning Open
Multi-party computation (MPC) based machine learning, referred to as multi-party learning (MPL), has become an important technology for utilizing data from multiple parties with privacy preservation. In recent years, in order to apply MPL …
View article: Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization
Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization Open
Multi-party training frameworks for decision trees based on secure multi-party computation enable multiple parties to train high-performance models on distributed private data with privacy preservation. The training process essentially inv…
View article: XRAD: Ransomware Address Detection Method based on Bitcoin Transaction Relationships
XRAD: Ransomware Address Detection Method based on Bitcoin Transaction Relationships Open
Recently, there is a surge in ransomware activities that encrypt users’ sensitive data and demand bitcoins for ransom payments to conceal the criminal’s identity. It is crucial for regulatory agencies to identify as many ransomware address…
View article: Exploring Unconfirmed Transactions for Effective Bitcoin Address Clustering
Exploring Unconfirmed Transactions for Effective Bitcoin Address Clustering Open
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View article: pMPL: A Robust Multi-Party Learning Framework with a Privileged Party
pMPL: A Robust Multi-Party Learning Framework with a Privileged Party Open
In order to perform machine learning among multiple parties while protecting\nthe privacy of raw data, privacy-preserving machine learning based on secure\nmulti-party computation (MPL for short) has been a hot spot in recent. The\nconfigu…
View article: A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin
A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin Open
Exploiting the anonymous mechanism of Bitcoin, ransomware activities demanding ransom in bitcoins have become rampant in recent years. Several existing studies quantify the impact of ransomware activities, mostly focusing on the amount of …
View article: Understanding Offline Password-Cracking Methods: A Large-Scale Empirical Study
Understanding Offline Password-Cracking Methods: A Large-Scale Empirical Study Open
Researchers proposed several data-driven methods to efficiently guess user-chosen passwords for password strength metering or password recovery in the past decades. However, these methods are usually evaluated under ad hoc scenarios with l…
View article: SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation
SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation Open
Nowadays, gathering high-quality training data from multiple data sources with privacy preservation is a crucial challenge to training high-performance machine learning models. The potential solutions could break the barriers among isolate…
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View article: An Explainable Password Strength Meter Addon via Textual Pattern Recognition
An Explainable Password Strength Meter Addon via Textual Pattern Recognition Open
Textual passwords are still dominating the authentication of remote file sharing and website logins, although researchers recently showed several vulnerabilities about this authentication mechanism. When a user creates or changes a passwor…
View article: FETMS: Fast and Efficient Trust Management Scheme for Information-Centric Networking in Internet of Things
FETMS: Fast and Efficient Trust Management Scheme for Information-Centric Networking in Internet of Things Open
As an innovative networking paradigm in the Internet of Things, information-centric networking (ICN) has improved security than the traditional IP networks. However, it still faces a large number of security threats, especially those threa…
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View article: Invisible Mask: Practical Attacks on Face Recognition with Infrared
Invisible Mask: Practical Attacks on Face Recognition with Infrared Open
Accurate face recognition techniques make a series of critical applications possible: policemen could employ it to retrieve criminals' faces from surveillance video streams; cross boarder travelers could pass a face authentication inspecti…
View article: A Privacy Preserving Data Aggregation Scheme to Investigate Apps Installment in Massive Mobile Devices
A Privacy Preserving Data Aggregation Scheme to Investigate Apps Installment in Massive Mobile Devices Open
Currently, mobile devices often try obtaining the list of installed applications to survey the install ratio for advertisers and app owners to evaluate the market share of their applications. However, adversaries could yet infer or even di…