Yandong Zheng

Paging is anticipated to play a pivotal role in realizing the 6G vision for the IoE (Internet of Everything) and uRLLC (ultra-reliable low-latency communications) by enhancing service quality and improving resource efficiency. Nevertheless, as 6G architectures evolve toward large-scale, distributed architectures, existing rule-driven, centralized paging mechanisms face severe challenges, including inefficiencies due to massive mobile-device access, incompatibility with distributed network architectures, and significant privacy concerns. To address these challenges, this paper proposes a privacy-preserving and personalized federated intelligent paging mechanism for 6G Networks (6G-PFIP). 6G-PFIP introduces a novel intelligent decision model to enhance paging efficiency for large-scale user equipment (UE). A personalized hierarchical federated paging architecture (HFPA) is further constructed to improve architectural compatibility and privacy by decomposing the centralized paging process into distributed layers. HFPA effectively addresses the challenge of Non-IID user data in 6G by designing region-level and local-level personalization and fairness-aware aggregation strategies. Furthermore, HFPA integrates local differential privacy (LDP) on demand to perturb shared model parameters, thereby mitigating the risk of privacy leakage. Experiments on a 6G Telecom Dataset show that 6G-PFIP achieves significant signaling resource savings (71.33%) and delay reductions (34.78%), while rigorous analysis confirms its privacy guarantees.

Searchable encryption (SE) allows users to perform private queries on encrypted databases. Although SE schemes can protect data privacy, some often pursue high performance while allowing certain leakages, such as search and access patterns. Exploiting such leakage together with other knowledge similar to the user’s database, an attacker can recover queries. State-of-the-art attacks (Nie et al., USENIX’ 24) on single-keyword queries achieve accuracies exceeding 90%. More recently, the community has focused on the more challenging attack of recovering multikeyword queries, with the most advanced attacks (Liu et al., TIFS’ 25) achieving over 80% accuracy. Although these attacks can effectively recover queries, they all rely on a large amount of similar document, requiring the attacker to possess documents equivalent in volume to the database. This naturally raises a question: Can we achieve higher-accuracy attacks using less similar data? Less information makes attacks easier to implement. Motivated by this, we present Mirage, an attack that recovers both single-keyword and multi-keyword queries while requiring only partial similar data. Our core idea is to first identify some special queries and design a series of novel algorithms to recover them. Then, partially reconstruct the database index and recover the remaining queries. Extensive experiments conducted across various real-world datasets demonstrate the effectiveness of our attack. The results show that when the attacker observes 51 time intervals and obtains only 0.5% of similar documents in each interval, Mirage achieves 91.6% and 95.4% accuracy on the Enron and Lucene datasets for single-keyword queries, respectively. For multi-keyword queries, Mirage achieves up to 90.7% and 93.5% recovery accuracy, respectively.

In the advancing field of information technology, the Industrial Internet of Things (IIoT) plays a pivotal role across various domains. To fully leverage the massive data generated by IIoT, the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm is well-suited for clustering large-scale time series data due to its incremental and order-sensitive design. Although aggregating multisource data on cloud servers becomes a practical and effective approach, it raises privacy concerns. Existing privacy-preserving BIRCH schemes designed to mitigate these concerns, however, rely on homomorphic encryption for atomic operations and reveal the insertion path of sample points, resulting in significant limitations in performance and security. To address these issues, we propose SBIRCH, a novel secure BIRCH scheme. Specifically, we first introduce oblivious hierarchical updates to preserve insertion paths. Then, we present a shuffling-based obfuscation technique to protect intermediate clustering results during node splitting. In addition, we optimize division operations on encrypted data, delivering high precision and significantly improving efficiency. Security analysis confirms that our scheme effectively protects multisource data, intermediate clustering results, and clustering relationships. While maintaining consistent security, experimental evaluations demonstrate that our scheme achieves substantial improvements in computational efficiency and markedly reduces communication overheads compared to existing schemes.

Fully homomorphic encryption (FHE) is a powerful technique that allows unlimited computations on encrypted data without decryption. However, FHE will incur huge computation and storage costs, making it difficult to be applied in real environments. To improve the efficiency of FHE, some optimized schemes have been proposed based on the trusted execution environment (TEE), which offer a lighter and lower overhead solution for FHE optimizations to a certain extent. However, they heavily rely on the confidentiality of the TEE, and their performance is still limited. To solve the above problems, we propose an efficient TEE-assistance CKKS scheme without bootstrapping, named TCKKS, which has the characteristics of security, efficiency, low memory, and scalability. First, to weaken the trust assumption of TEE, we consider TEE to be honest-but-curious, meaning the enclave's algorithm provider will execute the algorithm honestly but might monitor the data in the enclave. Based on this assumption, we design a lightweight secure multiplication protocol (SMP) and a secure rotation protocol (SRP) for TCKKS to efficiently achieve ciphertext multiplication and rotation operations. Then, to further improve the performance of TCKKS, we optimize the arithmetic operations and encryption/decryption operations based on the characteristics of our protocols. Moreover, we prove the security of SMP and SRP under the simulation-based real/ideal worlds model and further demonstrate the security of TCKKS based on the RLWE problem. In addition, extensive experiments indicate that TCKKS has a better performance than mainstream libraries, such as RNS-HEAAN, PALISADE and SEAL.

Promoting data sharing is one of the critical strategies for thriving in the digital age, and enormous demand for location-based services propels data sharing in the Internet of Vehicles (IoV), particularly in the case of integrating mobile crowdsensing (MCS). However, data security and privacy concerns are increasingly posing serious challenges to the development of data sharing. Although a slew of works have been designed to achieve secure data sharing in IoV, they are inadequate for addressing the privacy issues identified in the data sharing model and often suffer from performance limitations. In this work, we propose an efficient and secure data sharing scheme under the MCS-integrated IoV. Specifically, motivated by the distributed point function (DPF), we design a double-output DPF and leverage it to construct a secure updating scheme that protects full privacy while ensuring high efficiency. Then, based on the XOR filter and a series of subtle transformations, we carefully design a secure spatial test protocol to determine whether a point falls within an arbitrary spatial range efficiently. Afterward, we propose a secure retrieving protocol by using the idea of shared shuffling, in which the offline sub-protocol is presented to generate random masks, and the online sub-protocol is designed to quickly retrieve the desired data items. After formally proving the security of our proposed schemes, we experimentally evaluate their efficiency by comparing them with the alternative solutions, and the results indicate that our proposed schemes offer superior performance, particularly in terms of communication overheads.