Linguistic steganography (stego) seeks to conceal secret information within natural language text. However, existing methods often struggle to balance stego text quality with embedding efficiency, largely due to limitations in generation strategies and coding mechanisms. We propose SA-ANS, a self-adaptive linguistic steganography framework based on a self-adjusting Asymmetric Numeral System. SA-ANS allows user-specified embedding rates and uses probabilistic coding with adaptive candidate selection, dynamically tailoring the token pool to the language model’s probability distribution. This design produces fluent, semantically coherent stego text while preserving statistical indistinguishability from natural language. Extensive experiments on multiple benchmark datasets, evaluated across embedding efficiency, linguistic quality, statistical similarity, robustness to steganalysis, and human judgment, show that SA-ANS consistently outperforms state-of-the-art methods, demonstrating both effectiveness and practicality.

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving distributed machine learning, yet it remains highly vulnerable to model poisoning attacks. Malicious clients can submit manipulated updates to degrade model performance or implant hidden backdoors. While robust aggregation and attack detection have been extensively studied, efficiently recovering the global model after poisoning remains challenging. Retraining from scratch incurs prohibitive costs, whereas rollback-based approaches fail to eliminate incremental contamination. To overcome these limitations, we propose FedReconPP, a lightweight recovery framework. We design an importance-driven compressed storage mechanism to significantly reduce overhead. We introduce a time-decayed contamination tracking mechanism with rigorous theoretical foundation that captures and mitigates incremental poisoning and develop a quality-aware privacy-preserving recovery strategy with formal differential privacy guarantees to strengthen robustness against inference attacks while maintaining model utility. Extensive experiments on multiple datasets, demonstrate that FedReconPP achieves reliable recovery with substantially lower resource consumption, offering practicality and strong protection for federated learning systems.

In recent years, cross-device federated learning (FL), particularly in the context of Internet of Things (IoT) applications, has demonstrated its remarkable potential. Despite significant efforts, empirical evidence suggests that FL algorithms have yet to gain widespread practical adoption. The primary obstacle stems from the inherent bandwidth overhead associated with gradient exchanges between clients and the server, resulting in substantial delays, especially within communication networks. To deal with the problem, various solutions are proposed with the hope of finding a better balance between efficiency and accuracy. Following this goal, we focus on investigating how to design a lightweight FL algorithm that requires less communication cost while maintaining comparable accuracy. Specifically, we propose a Sketch-based FL algorithm that combines the incremental singular value decomposition (ISVD) method in a way that does not negatively affect accuracy much in the training process. Moreover, we also provide adaptive gradient error accumulation and error compensation mechanisms to mitigate accumulated gradient errors caused by sketch compression and improve the model accuracy. Our extensive experimentation with various datasets demonstrates the efficacy of our proposed approach. Specifically, our scheme achieves nearly a 93% reduction in communication cost during the training of multi-layer perceptron models (MLP) using the MNIST dataset.

Searchable symmetric encryption (SSE) supporting conjunctive queries has garnered significant attention over the past decade due to its practicality and wide applicability. While extensive research has addressed common leakages, such as the access pattern and search pattern, efforts to mitigate these vulnerabilities have primarily focused on structural issues inherent to scheme construction. In this work, we shift the focus to a less explored yet critical leakage stemming from users’ inherent querying behaviors: query correlation. Originally introduced by Grubbs et al. [USENIX SEC’20], formally defined by Oya and Kerschbaum [USENIX SEC’22], and leveraged to mount a high-success query recovery attack against single-keyword SSE, query correlation raises a crucial question: does it pose a similar threat to the security of conjunctive SSE? To tackle this issue, we undertake two key efforts. First, we generalize the notion of query correlation in the context of conjunctive SSE, introducing the “generalized query correlation pattern”, which captures the co-occurrence relationships among queried tokens within a conjunctive query. Second, we develop a new passive query recovery attack, QCCK, which exploits both the search pattern and generalized query correlation pattern to infer the mapping between tokens and keywords. Comprehensive evaluations on the Enron dataset confirm QCCK’s efficacy, achieving a query recovery rate of approximately 80% with a keyword universe size ranging from 200 to 1000 and an observed query size between 5000 and 50,000. These findings highlight the significant threat posed by query correlation in conjunctive SSE and underscore the urgent need for robust countermeasures.

Federated edge learning (FEL) has emerged as a distributed machine learning paradigm that enables devices to jointly train a shared global model in the Industrial Internet of Things (IIoT), which greatly accelerates the advancement of Industrial 4.0. However, FEL is vulnerable to the colluded backdoor attack (CBA) at high percentages of poisonous devices and attack intensity. Meanwhile, conventional strategies against general BAs aim to mitigate the impact of degrading the accuracy of benign clients by propagating correct local updates, but they can hardly prevent CBAs. Moreover, recent work defends against CBAs at the cost of slower convergence. To tackle the above vulnerabilities, we propose RDCBA-FEL, which effectively identifies and corrects malicious updates from all local ones. Firstly, RDCBA-FEL restricts common features in poisonous updates, such as amplified magnitudes or similar descent directions. Secondly, RDCBA-FEL adopts the residual-based attack detection mechanism to identify and convert malicious updates into benign ones, thus speeding up model convergence. Thirdly, RDCBA-FEL employs the beta-based reputation model to average the weights of local updates, guaranteeing that benign updates have priority over poisoned ones. Moreover, since historical reputations can negatively affect benign weight allocation, a distributed time-decay attention mechanism is used to flexibly adjust FEL to make the model more focused on the current reputation. Extensive evaluations on five benchmark datasets show the robustness of RDCBA-FEL against advanced attacks compared to eleven state-of-the-art schemes.

Linear code, a foundational construct extensively employed in communication, data transmission, and error correction, has been the subject of rigorous study for decades. Despite significant academic successes and widespread adoption, recent studies show that decoding methods for general linear codes, such as syndrome decoding, require the storage and search of large decoding tables, leading to inefficiencies. To mitigate this, Debris-Alazard et al. first proposed adapting Babai's algorithm and the LLL algorithm from lattice theory to binary codes, achieving considerable performance gains. Inspired by this, in this paper, we aim to explore the design of more general linear codes to overcome the limitations of baseline binary codes and enhance their applicability in more advanced applications such as DNA storage and 5G communication systems. To address this gap, we extend the foundational domain and decoding algorithms from lattices to q-ary codes. Specifically, we define a new fundamental domain and propose a polynomial-time decoding algorithm, RedtoFun. To validate our findings, we conduct a series of experiments to evaluate its real-world performance. The results demonstrate that our optimized RedtoFun algorithm surpasses the syndrome decoding scheme in terms of memory overhead and runtime while maintaining performance on par with the SizeRed decoding scheme.

Multi-source multi-client (M/M) searchable encryption has drawn increasing attention as data sharing becomes prevalent in the digital economics era. It allows data from multiple sources to be securely outsourced to third parties and queried by authorized clients. In response to these demands, various schemes sprung up in the last few years. However, empirical results show that they suffer from performance limitations. Specifically, they either require per-interaction in per-query between data sources and clients or time-consuming public-key encryption. To address these issues, we propose a searchable encryption scheme that allows authorized clients to efficiently search encrypted data from multiple sources. Compared to previous schemes, our design reduces the interaction overhead of authorization and query with the aid of a set-constrained pseudo-random function. Given practical considerations in the M/M setting, we further refine the forward privacy (FP) as “FP with client” and “FP with server” for data addition. To achieve these new security notions, we construct a new M/M scheme only with efficient symmetric cryptographic tools. We perform a formal security analysis of the proposed schemes and implement them to compare with prior arts. The theoretical and experimental results confirm that our designs are practical with lower communication and computation overhead.

Edge computing brings data computation and storage closer to the mobile device to save response time for decision making. After being processed at the edge, commonly, the data will be uploaded to the cloud for further enriched analysis. For privacy concerns, local devices may encrypt the collected data before sending it to the cloud server. However, this treatment increases the servers processing time and makes it hard to pick out the desired data. In this paper, to address this problem, we design an encrypted data validation scheme, which enables the edge to clean the encrypted data to be uploaded. Because edge computing encompasses numerous edge devices from different service providers, we explore the public-key cryptographic mechanism to implement our secure data validation scheme. Considering potential risks from quantum computers, we propose to leverage ideal lattice to realize our protocol, which reaches better performance in both time and storage for edge devices. Extensive evaluation results show that our proposed proposal achieves considerable performance improvements in terms of communication and computation aspects. Particularly, compared to prior work, nearly 123x-238x speed up is achieved in our key derivation procedure and the storage cost of the secret key is reduced from 547MB to 1.7MB.

[J1C2 Presentation Abstract at IEEE SERVICES 2021 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2021.3111208]

Multi-Writer Searchable Encryption, also known as public-key encryption with keyword search(PEKS), serves a wide spectrum of data sharing applications. It allows users to search over encrypted data encrypted via different keys. However, most of the existing PEKS schemes are built on classic security assumptions, which are proven to be untenable to overcome the threats of quantum computers. To address the above problem, in this paper, we propose a lattice-based searchable encryption scheme from the learning with errors (LWE) hardness assumption. Specifically, we observe that the keys of each user in a basic scheme are composed of large-sized matrices and basis of the lattice. To reduce the complexity of key management, our scheme is designed to enable users to directly use their identity for data encryption. We present several optimization techniques for implementation to make our design nearly practical. For completeness, we conduct rigorous security, complexity, and parameter analysis on our scheme, and perform comprehensive evaluations at a commodity machine. With a scenario of 100 users, the cost of key generation for each user is 125s, and the cost of searching a document with 1000 keywords is 13.4ms.

Searchable encryption (SE) is a practical crypto-graphic primitive to build encrypted databases. Recently there has been much attention in leakage-abuse attacks against SE. Among others, attacks based on inference of keyword frequency can easily identify query keywords from the access pattern, i.e., query results. To mitigate these attacks, database padding is considered as a conceptually simple yet effective counter-measure. Unfortunately, none of the existing studies formally understand the relationship between padding security strength and its overhead. Also, how to craft padding is not restricted in current countermeasures, where bogus files are likely to be distinguishable from real ones. In this paper, we propose an information theory based framework to analyse the security strength under certain padding overhead. First, we leverage relative entropy to measure the “closeness” between the distributions of the original dataset and padded dataset. Second, we quantity the attack efforts against padding countermeasures by entropy analysis. Apart from theoretical findings, we further devise an algorithm via outlier detection for padding generation, which considers both the padded dataset distribution and the similarity between real and bogus files. Evaluations on a real-world dataset confirm our theoretical results and demonstrate the efficiency and effectiveness of our proposed padding generation algorithm.