Research

This academic paper proposes a policy-based conjunctive scheme, which is a method for managing how groups of people can collectively decide to delete shared data they all own together. The research addresses the challenge of 'digital forgetting' (the ability to have data permanently removed) when multiple parties have rights to the same information, requiring agreement from all co-owners before deletion occurs.

This academic paper, published in July 2026, presents research on laconic attribute-based PSI (private set intersection, a technique that lets two parties find common items in their datasets without revealing the full datasets to each other) applied to authenticated inputs. The work appears to focus on theoretical cryptographic methods for secure data comparison while maintaining privacy and verifying that the data being compared is legitimate.

This research paper examines how well attack mitigations (security protections built into code) actually work in Rust and Go, two programming languages designed to be memory-safe (meaning they prevent common memory-related bugs that attackers often exploit). The study analyzes real compiled programs to see whether these language protections hold up against real-world attacks.

FastBOC is a covert communication system (a method for secretly transmitting data) that combines blockchain (a distributed ledger technology) and onion networks (systems that route data through multiple layers to hide user identity). The system uses the blockchain only to send small signal messages while using onion networks for the actual secret data, which reduces costs and prevents permanent exposure of sensitive information on the blockchain.

This paper addresses the problem that trusted execution environments (TEEs, like TrustZone, which are isolated secure areas in processors that protect sensitive data from untrusted software) typically have poor support for device drivers. The researchers propose a "twin driver" approach where matching drivers run in both the TEE and the untrusted operating system, supported by analysis tools and a sandbox environment that keeps the TEE minimal and secure while allowing efficient driver operation.

This research paper examines how people with and without prior victimization differ in their ability to detect scams. The study, published in Computers & Security in May 2026, explores whether past experience being scammed makes individuals better at identifying fraudulent attempts.

Researchers created a testing framework to evaluate how deepfakes (AI-generated fake videos or images of people) can fool face recognition systems (AI that identifies people by their faces). The study found that deepfake attacks succeed over 70% of the time, sometimes exceeding 90%, and discovered that attack success depends more on how well attackers can control the person's identity in the fake content rather than on how realistic the deepfake looks visually.

This research paper introduces PVLM, a new method for identifying which AI system created a deepfake (fake video or image of a person's face) by analyzing how well different generators preserve facial features. The approach combines vision-language models (AI systems that understand both images and text) with face parsing (analyzing individual facial components) and dynamic contrastive learning (a training technique that groups similar items together while separating different ones) to better recognize deepfakes from unseen advanced generators like diffusion models (AI systems that create images by gradually removing noise).

This research paper presents MPV, a method for restricting access to master keys in multi-user Paillier systems (a cryptographic system that allows certain calculations on encrypted data without decrypting it first) by using mixed ciphertexts (encrypted data created with different encryption methods combined). The approach aims to improve security by preventing unauthorized parties from decrypting sensitive information even if they gain access to the master key.

AI agents (software systems that can plan and take actions over time) that retain memory between sessions create a security risk called Memory & Context Poisoning, where attackers can inject malicious instructions into persistent storage that the agent continues to trust and follow in future interactions. Researchers found a vulnerability called MemoryTrap in Claude Code where a developer could unknowingly approve a malicious dependency that would persist in the agent's memory and configuration files, poisoning the agent's behavior across multiple projects and sessions. The core problem is that agents treat stored memory, configuration files, and hooks as trustworthy guidance without validating whether they contain attacker-controlled content.

This academic publication discusses PUF (physically unclonable functions, unique fingerprints built into hardware chips that are nearly impossible to copy) optimization methods for authenticating IoT devices (internet-connected devices like smart home sensors). The research focuses on improving how these hardware-based security features can be used to verify that IoT devices are genuine and trustworthy.

This academic paper presents a method using AI to extract entities (named items like organizations or IP addresses) and relationships between them from threat intelligence data about APT (advanced persistent threat, a type of sophisticated cyberattack) attacks. The researchers developed a system to help security analysts automatically identify and organize complex attack patterns from unstructured text documents.

This research paper presents a method for optimally placing honeypots (decoy systems designed to attract and monitor attackers) in networks where multiple attackers operate simultaneously, using Bayesian Stackelberg Games (a mathematical framework for strategic decision-making under incomplete information). The approach aims to help defenders allocate honeypots more effectively by predicting attacker behavior and making strategic placement decisions.

This research paper presents algorithms for solving the weight stable set attack problem, which involves removing nodes from a social network while staying within a budget constraint to minimize the remaining network's influence potential. The authors develop a 2α-approximation algorithm (an algorithm guaranteed to find solutions within twice the optimal answer) for networks without odd cycles and extend it to general networks, comparing it against a genetic algorithm (a problem-solving technique inspired by natural evolution) through experiments on both artificial and real-world networks.

This research paper addresses scalability problems in blockchain payment systems by proposing RD-PCN, a payment channel network (PCN, a system that allows cryptocurrency transactions without recording every transaction on the main blockchain) designed for nodes with different capabilities. The solution uses multi-party payment channels (MPCs, channels that connect multiple users together) and privacy-preserving routing (sending payments through the network while hiding transaction details) to improve payment success rates and fund utilization in real-world blockchain networks.

This research studies whether adversarial perturbations (small, intentional noise added to audio that tricks speaker recognition systems into misidentifying who is speaking) can be removed from speech. The study tested three scenarios based on how much information a defense system has about the attack: knowing nothing about it, having partial information, and having complete information. The results showed that removing these perturbations is only possible when the defense system has full knowledge of how the attack was generated, while partial or no knowledge makes complete removal difficult or impossible.

This paper presents ADAPT, a framework that improves blockchain Proof of Stake (PoS, a method where validators secure the network based on how much cryptocurrency they own) systems by allowing dynamic changes to voting power without requiring trusted intermediaries or system downtime. The solution uses Generalized Lagrange Interpolation (GLI, a mathematical technique that encodes voting weights as polynomial calculations) applied to the FROST threshold signature scheme (a cryptographic method where multiple parties must cooperate to sign transactions), achieving faster weight and threshold adjustments compared to existing approaches.

This paper addresses a challenge where stereo-matching networks (AI systems that estimate depth by comparing two images) perform well on synthetic training data but struggle with real-world images due to domain gap (the difference between training and real-world data). The researchers propose ADASM, a method using adversarial domain alignment (exposing the model to worst-case scenarios during training to improve robustness) to make these networks generalize better to unseen real-world data without requiring fine-tuning.

Many students prefer free videos and AI tools over reading security books, even though expert-written books often provide clear and deep knowledge about security. The source encourages students to recognize that security books remain valuable learning resources despite newer alternatives.