
Explore automatic vulnerability detection in LTE networks using documentation analysis, covering network architecture, hazard indicators, and attack models.

Explore techniques for detecting filter list evasion using JavaScript signatures, focusing on event-loop-turn granularity and privacy-relevant aspects of web tracking.

Explore lower bounds for differentially private machine learning, focusing on DPSGD, membership inference, and gradient poisoning attacks in this IEEE presentation.

Explore supervised machine learning techniques for binary analysis, focusing on architecture and code section detection to enhance reverse engineering and security analysis.

Empirical analysis of data-saving mobile browsers, exploring their effectiveness, impact on usability, and potential security risks. Examines proxy server configurations, privacy concerns, and proposes improved designs.

Explore KARONTE, an innovative tool for detecting security vulnerabilities in embedded firmware through multi-binary interaction analysis, enhancing IoT device protection.

Comprehensive methodology for cloud providers to assess DRAM vulnerability to Rowhammer attacks, including near-optimal instruction sequences and row adjacency mapping techniques for modern Intel platforms.

Explore fast patch propagation in software repositories, focusing on security patches, solution requirements, and implementation techniques for efficient and safe updates.

Explore an extensible framework for assessing IC layout vulnerability to additive trojans, covering hardware security, development processes, and preventive measures.

Uncover hidden data collection in modern cars using AutoCAN tool. Reveals GPS tracking, occupant info, usage stats, and remote control capabilities through automated network analysis and reverse engineering.

Unveils a robust de-anonymization technique for Bluetooth Classic devices, demonstrating vulnerabilities in privacy guarantees and enabling tracking of piconet masters through SDR-based sniffing.

Introduces private resource allocators to prevent allocation-based side-channel attacks in systems like anonymous messaging, with various constructions offering different privacy guarantees and trade-offs in efficiency.

Explores vulnerabilities in provably secure onion routing protocols, analyzes flaws in existing proof strategies, and proposes improved properties to enhance anonymity and security in these systems.

Explore a programming framework for differential privacy, balancing data protection and analysis accuracy through novel static tracking and taint analysis techniques.

Explores a novel screen attack method using mmWave sensing to remotely capture display content through walls, bypassing traditional security measures and demonstrating vulnerabilities in isolated environments.

Analyzes security of smartphone unlock PINs, comparing 4- and 6-digit options, and evaluating effectiveness of blacklists in enhancing protection against guessing attacks.

Reverse engineering DDIO to demonstrate NetCAT, a network-based cache attack enabling remote keystroke timing attacks and covert channels, highlighting security risks in modern Intel processors.

Experts discuss essential privacy and security information for IoT device labels, proposing a two-layer design to help consumers make informed purchase decisions.

Explores differential privacy as a causal property, offering a simple characterization without data independence assumptions and resolving prior disagreements about its consequences and applications.

Explores collaborative machine learning on distributed private data using differentially-private gradients, quantifying trade-offs between privacy and utility, and validating performance predictions with real-world financial datasets.

Principled detection of speculative information flows in computer systems, focusing on security against Spectre-like attacks through speculative non-interference and symbolic execution techniques.