arXiv subjects

Qingfeng Zhou

Publications and source records attributed to Qingfeng Zhou.

4 recordsLinked to original sources

arXiv2025

Discovering Top-k Periodic and High-Utility Patterns

With a user-specified minimum utility threshold (minutil), periodic high-utility pattern mining (PHUPM) aims to identify high-utility patterns that occur periodically in a transaction database. A pattern is deemed periodic if its period aligns with the periodicity constraint set by the user. However, users may not be interested in all periodic high-utility patterns (PHUPs). Moreover, setting minutil in advance is also a challenging issue. To address these issues, our research introduces an algorithm called TPU for extracting the most significant top-k periodic and high-utility patterns that may or may not include negative utility values. This TPU algorithm utilizes positive and negative utility lists (PNUL) and period-estimated utility co-occurrence structure (PEUCS) to store pertinent itemset information. It incorporates the periodic real item utility (PIU), periodic co-occurrence utility descending (PCUD), and periodic real utility (PRU) threshold-raising strategies to elevate the thresholds rapidly. By using the proposed threshold-raising strategies, the runtime was reduced by approximately 5\% on the datasets used in the experiments. Specifically, the runtime was reduced by up to 50\% on the mushroom\_negative and kosarak\_negative datasets, and by up to 10\% on the chess\_negative dataset. Memory consumption was reduced by about 2\%, with the largest reduction of about 30\% observed on the mushroom\_negative dataset. Through extensive experiments, we have demonstrated that our algorithm can accurately and effectively extract the top-k periodic high-utility patterns. This paper successfully addresses the top-k mining issue and contributes to data science.

cs.DB↗

arXiv2025

Utility-based Privacy Preserving Data Mining

With the advent of big data, periodic pattern mining has demonstrated significant value in real-world applications, including smart home systems, healthcare systems, and the medical field. However, advances in network technology have enabled malicious actors to extract sensitive information from publicly available datasets, posing significant threats to data providers and, in severe cases, hindering societal development. To mitigate such risks, privacy-preserving utility mining (PPUM) has been proposed. However, PPUM is unsuitable for addressing privacy concerns in periodic information mining. To address this issue, we innovatively extend the existing PPUM framework and propose two algorithms, Maximum sensitive Utility-MAximum maxPer item (MU-MAP) and Maximum sensitive Utility-MInimum maxPer item (MU-MIP). These algorithms aim to hide sensitive periodic high-utility itemsets while generating sanitized datasets. To enhance the efficiency of the algorithms, we designed two novel data structures: the Sensitive Itemset List (SISL) and the Sensitive Item List (SIL), which store essential information about sensitive itemsets and their constituent items. Moreover, several performance metrics were employed to evaluate the performance of our algorithms compared to the state-of-the-art PPUM algorithms. The experimental results show that our proposed algorithms achieve an Artificial Cost (AC) value of 0 on all datasets when hiding sensitive itemsets. In contrast, the traditional PPUM algorithm yields non-zero AC. This indicates that our algorithms can successfully hide sensitive periodic itemsets without introducing misleading patterns, whereas the PPUM algorithm generates additional itemsets that may interfere with user decision-making. Moreover, the results also reveal that our algorithms maintain Database Utility Similarity (DUS) of over 90\% after the sensitive itemsets are hidden.

cs.DB↗

arXiv2023

SBMA: A Multiple Access Scheme Combining SCMA and BIA for MU-MISO

Sparse Code Multiple Access (SCMA) and Blind Interference Alignment (BIA) are key enablers for multi-user communication, yet each suffers from distinct limitations: SCMA faces high complexity and limited multiplexing gain, while BIA requires a long temporal channel pattern and incurs significant decoding delay. This paper proposes SBMA (Sparsecode-and-BIA-based Multiple Access), a novel framework that synergizes SCMA's diversity and BIA's multiplexing while addressing their drawbacks. We design two decoders: a low-complexity two-stage decoder (Zero-forcing + Message Passing Algorithm (MPA)) and a Joint MPA (JMPA) decoder leveraging a virtual factor graph for improved BER. Theoretical analysis derives closed-form BER expressions for a 6-user 2x1 MISO system, validated by simulations. Compared to existing schemes, SBMA with JMPA achieves a diversity gain equivalent to STBC-SCMA and a multiplexing gain comparable to BIA, while simultaneously offering enhanced privacy (relative to STBC-SCMA) and reduced reliance on channel coherence time (compared to BIA). These advancements position SBMA as a compelling solution for next-generation wireless communication systems, particularly in IoT applications demanding high throughput, robust data privacy, and adaptability to dynamic channel conditions.

cs.IT↗

arXiv2011

Non-memoryless Analog Network Coding in Two-Way Relay Channel

Physical-layer Network Coding (PNC) can significantly improve the throughput of two-way relay channels. An interesting variant of PNC is Analog Network Coding (ANC). Almost all ANC schemes proposed to date, however, operate in a symbol by symbol manner (memoryless) and cannot exploit the redundant information in channel-coded packets to enhance performance. This paper proposes a non-memoryless ANC scheme. In particular, we design a soft-input soft-output decoder for the relay node to process the superimposed packets from the two end nodes to yield an estimated MMSE packet for forwarding back to the end nodes. Our decoder takes into account the correlation among different symbols in the packets due to channel coding, and provides significantly improved MSE performance. Our analysis shows that the SNR improvement at the relay node is lower bounded by 1/R (R is the code rate) with the simplest LDPC code (repeat code). The SNR improvement is also verified by numerical simulation with LDPC code. Our results indicate that LDPC codes of different degrees are preferred in different SNR regions. Generally speaking, smaller degrees are preferred for lower SNRs.

cs.IT↗