Ghost introduces a manifold-aligned framework to generate plausible yet unlearnable synthetic check-in trajectories, significantly degrading the accuracy of next-POI prediction models without sacrific…

The paper introduces a mathematical and cryptographic framework for exactly recovering a single, noisy, high-dimensional discrete path from aggregated and incomplete observable data.

The paper demonstrates that cryptographically undetectable backdoors can be embedded into modern, state-of-the-art neural networks by exploiting inherent, latent geometric properties of the learned re…

TrajGuard is a novel, training-free defense framework that detects jailbreaks by monitoring the progressive risk signals embedded in the hidden-state trajectories of tokens during the LLM decoding pro…

The paper introduces diffGHOST, a conditional diffusion model that generates synthetic, privacy-preserving mobility trajectories by explicitly mitigating sample memorization in the latent space.

The paper introduces ActInv and PAF to systematically analyze and quantify privacy leakage from intermediate activations during split inference of LLMs, proposing PriPert for enhanced defense.

This paper provides the first systematic threat analysis of State-Space Models (SSMs) in safety-critical applications, introducing novel attack classes and formal metrics to quantify their security an…

The paper introduces Sparse Backdoor, a novel supply-chain attack that embeds a provably undetectable backdoor into pre-trained image classifiers by injecting structured sparse perturbations.

The paper addresses the vulnerability of zero-knowledge proximity proofs in stateful systems by proposing Zairn-ZKP, a method that embeds operational context (like drop identity and policy version) di…

The paper introduces stronger cryptographic notions, quantum localization and trajectory verification, to robustly certify a quantum entity's position and movement through spacetime.

The paper develops a structurally justified framework for measuring Quantum Cryptographic Exposure (HNDL) by showing that the compromise probability factorizes into distinct, interacting components ba…

The paper introduces Asymmetric Langevin Unlearning (ALU), a novel framework that uses public data to significantly reduce the utility loss typically associated with certified machine unlearning, enab…

The paper proposes VerFU, a client-verifiable federated unlearning framework for low-altitude wireless networks that allows devices to ensure the server accurately removes their historical data contri…

The paper introduces $(l, b)$-inextractability, a new formal measure that demonstrates that standard indistinguishability properties are insufficient for guaranteeing protection against data extractio…

The paper introduces DiffusionHijack, a supply-chain backdoor attack that compromises the PRNG used by diffusion models to deterministically control generated images, which is successfully mitigated b…

The paper introduces a multi-surface evidence framework to provide comprehensive observability for post-quantum TLS migration, enabling robust measurement of session behavior and endpoint capabilities…

This study longitudinally evaluates the adversarial robustness of Android malware detection systems over a decade, finding that temporal separation significantly degrades robustness due to concept dri…

The paper demonstrates that even a casual attacker with basic IT skills can perform sophisticated privacy attacks on smart-home networks, extracting detailed daily routines and personal information fr…

This paper introduces TC-UMIA, a novel tri-class membership inference attack, demonstrating that machine unlearning can leak privacy risks to the retained data set, and evaluates defense mechanisms to…

The paper introduces public-decay Homomorphic State Space Models (HSSMs) that enable efficient, high-accuracy sequence inference directly on encrypted data, significantly outperforming existing encryp…