The high overhead of fault-tolerant measurement sequences (FTMSs) poses a major challenge for implementing quantum stabilizer codes. Here, we address this problem by constructing efficient FTMSs for the class of quantum Hamming codes $[\![2^r-1, 2^r-1-2r, 3]\!]$ with $r=3k+1$ ($k \in \mathbb{Z}^+$). Our key result demonstrates that the sequence length can be reduced to exactly $2r+1$-only one additional measurement beyond the original non-fault-tolerant sequence, establishing a tight lower bound. The proposed method leverages cyclic matrix transformations to systematically combine rows of the initial stabilizer matrix and preserving a self-dual CSS-like symmetry analogous to that of the original quantum Hamming codes. This induced symmetry enables hardware-efficient circuit reuse: the measurement circuits for the first $r$ stabilizers are transformed into circuits for the remaining $r$ stabilizers simply by toggling boundary Hadamard gates, eliminating redundant hardware. For distance-3 fault-tolerant error correction, our approach simultaneously reduces the time overhead via shorting the FTMS length and the hardware overhead through symmetry-enabled circuit multiplexing. These results provide an important advance towards the important open problem regarding the design of minimal FTMSs for quantum Hamming codes and may shed light on similar challenges in other quantum stabilizer codes.

Variable-rate coding schemes that support a variety of different rates while maintaining the same fundamental encoder/decoder architectures are of great interest in practical communication systems. Similar error-correcting code schemes are also in demand in quantum settings. However, generally, it is difficult to introduce the variable-rate coding schemes into the quantum coding domain attributed to two challenges: Obtaining new quantum codes from old ones systematically and sharing encoder-decoder components among the newly obtained quantum codes. In this correspondence, a multiple-rate coding scheme is introduced into the quantum coding domain, we achieve this goal by providing a systematic method to construct new non-homogeneous quantum LDPC codes of CSS type from an old one (named as the mother code) whose classical parity-check matrix (PCM) is row-circulant. The basic idea is to split rows of the highest-rate stabilizer elaborately to produce the stabilizer for lower rates, which ensures that the generated code family owns the same code length. More importantly, thanks to the nested stabilizer structures possessed by the resulting quantum codes, our method also enables a scalable stabilizer circuit to be shared among them.

Due to the transparency of the wireless channel, users in multiple-key environment are vulnerable to eavesdropping during the process of uploading personal data and re-encryption keys. Besides, there is additional burden of key management arising from multiple keys of users. In addition, profile matching using inner product between vectors cannot effectively filter out users with ulterior motives. To tackle the above challenges, we first improve a homomorphic re-encryption system (HRES) to support a single homomorphic multiplication and arbitrarily many homomorphic additions. The public key negotiated by the clouds is used to encrypt the users’ data, thereby avoiding the issues of key leakage and key management, and the privacy of users’ data is also protected. Furthermore, our scheme utilizes the homomorphic multiplication property of the improved HRES algorithm to compute the cosine result between the normalized vectors as the standard for measuring the users’ proximity. Thus, we can effectively improve the social experience of users.

Data clustering is the unsupervised classification of data records into groups. As one of the steps in data analysis, it has been widely researched and applied in practical life, such as pattern recognition, image processing, information retrieval, geography, and marketing. In addition, the rapid increase of data volume in recent years poses a huge challenge for resource-constrained data owners to perform computation on their data. This leads to a trend that users authorize the cloud to perform computation on stored data, such as keyword search, equality test, and outsourced data clustering. In outsourced data clustering, the cloud classifies users’ data into groups according to their similarities. Considering the sensitive information in outsourced data and multiple data owners in practical application, it is necessary to develop a privacy-preserving outsourced clustering scheme under multiple keys. Recently, Rong et al. proposed a privacy-preserving outsourced k-means clustering scheme under multiple keys. However, in their scheme, the assistant server (AS) is able to extract the ratio of two underlying data records, and key management server (KMS) can decrypt the ciphertexts of owners’ data records, which break the privacy security. AS can even reduce all data records if it knows one of the data records. To solve the aforementioned problem, we propose a highly secure privacy-preserving outsourced k-means clustering scheme under multiple keys in cloud computing. In this paper, noncolluded cloud computing service (CCS) and KMS jointly perform clustering over the encrypted data records without exposing data privacy. Specifically, we use BCP encryption which has additive homomorphic property and AES encryption to double encrypt data records, where the former cryptosystem prevents CCS from obtaining any useful information from received ciphertexts and the latter one protects data records from being decrypted by KMS. We first define five protocols to realize different functions and then present our scheme based on these protocols. Finally, we give the security and performance analyses which show that our scheme is comparable with the existing schemes on functionality and security.