Privacy-Preserving Record-Linkage (PPRL) techniques provide an error-tolerant way of linking data records from different sources based on quasi-identifiers while keeping these identifying attributes private. Many practical use cases require non-interactive PPRL, where the data holders merely prepare their data and outsource data linkage to a third party. While many such schemes have been proposed and some are even used in practice, almost all of them are either insecure or impractical for various reasons. This work presents a novel PPRL scheme which utilizes a technique called ‘Polynomial Sparsity Testing’. We provide extensive theoretical and experimental evidence for the high linkage quality and security of the scheme, which is on par with the state of the art: Using comparable parameter choices in terms of security, our scheme achieves a F_1 -measure of 94.3% at a precision of 100% , while comparing a record pair takes less than 0.4 ms.
Symmetric functions are an important class of functions that includes many interesting functions. Constructing efficient card-based protocols for symmetric functions is considered an important research topic in card-based cryptography. In 2020, Ruangwises and Itoh designed a protocol for symmetric functions f: {0,1}^n → R using 2n + 2 cards of a two-colored deck. Whether the number of cards in this protocol could be reduced is an open problem. In this paper, we design protocols for symmetric functions using 2n + 1 cards of a two-colored deck, which solve the above problem affirmatively. We also propose protocols for symmetric functions using 2n cards of a three-colored deck, while the existing 2n-card protocol requires a four-colored deck. By further restricting the class of functions to {0} -partially doubly symmetric functions, we can construct even more efficient protocols. In particular, we construct protocols for {0} -partially doubly symmetric functions using 2n cards of a two-colored deck, while the existing protocol requires 2n+1 cards.
The rise of autonomous AI agents powered by large language models (LLMs) has been accompanied by new frameworks for integrating these agents with external tools and data. One such framework is Anthropic’s Model Context Protocol (MCP), a recently introduced open standard that enables AI assistants to connect with a wide variety of external systems. While MCP unlocks powerful capabilities for agentic AI, it also dramatically expands the supply chain threat surface. In this paper, we investigate supply chain threats in the MCP ecosystem, a rapidly emerging security frontier as community-driven development and open-source MCP servers become prevalent. We identify and categorize major supply chain threats in the MCP ecosystem and validate representative scenarios via proof-of-concept attacks. In particular, we demonstrate how malicious MCP servers, as well as hostile data inputs, can be used to intentionally trigger unauthorized or harmful behaviors, such as sensitive data exfiltration or security policy violations. Finally, we outline two complementary avenues for mitigating MCP supply-chain threats. First, a specification- and document-based validation framework addresses code-driven threats by statically verifying that an MCP tool’s implementation aligns with its declared interface and behavior. This method has been prototyped and shown to detect functional inconsistencies in real-world tools. Second, we propose the conceptual design of lightweight runtime validation agents—supervisory components that monitor prompt flows, tool responses, and runtime context to intercept data-driven threats. Together, these layers—proven static analyzers and envisioned runtime validators—form a cohesive foundation for securing AI-agent infrastructures in the MCP ecosystem. By identifying novel attack surfaces and proposing layered defenses, our work represents an early step toward framing supply chain threat dimensions in MCP and AI agent security, and contributes to the ongoing discourse on mitigation strategies for AI-integrated supply chains.
As online gaming and esports platforms grow in popularity and complexity, concerns over account security and user authentication are increasing. This study explores the use of behavioral biometrics to identify individual players based solely on their in-game actions in Counter-Strike: Global Offensive (CS:GO) using data from professional tournament matches, with a particular focus on the impact of sequence length on identification performance. We propose a machine learning framework that aggregates gameplay data into fixed length sequences and extracts interpretable features related to movement, aiming, and economic behavior. To investigate how temporal context influences model performance, we evaluate binary classifiers across a range of sequence durations, from a few seconds to several minutes. For each duration, behavioral data is aggregated into a single feature vector using statistical summaries, such as mean and count, to represent player behavior over the window. Importantly, the window sizes overlap every five seconds of gameplay, generating more data for the model and allowing for finer-grained analysis. To assess generalizability, models are tested exclusively on unseen matches occurring after the training period. Results show that identification accuracy is sensitive to sequence length, with longer sequences generally resulting in higher predictions. This case study contributes to the development of behavioral authentication systems in gaming by demonstrating how sequence length and feature design influence player identification performance.
Local Differential Privacy (LDP) has recently emerged as a popular standard for privacy-preserving data collection, and bitvector-based LDP protocols such as RAPPOR and OUE are widely used in both academic and industrial applications. To evaluate LDP protocols and applications, researchers commonly rely on simulation-based experiments, where multiple users’ perturbations are simulated sequentially on one computer. While faithful to protocol definitions, this approach incurs substantial execution times, especially for large user populations and domains. To address this concern and enable fast simulations, in this paper, we propose a novel simulation methodology for bitvector-based LDP protocols. Our key insight is to model the collective effect of randomized perturbation using Binomial random variables, avoiding the need to simulate each user individually. We theoretically and empirically show that this strategy reduces computational complexity while producing unbiased estimations with identical variance to RAPPOR and OUE. Furthermore, we empirically show that our method reduces execution times from several minutes to less than a second, yielding multiple orders of magnitude improvement. Overall, our work offers a fast and scalable method for simulating bitvector-based LDP protocols, with direct applicability to existing works and simulation platforms.
