Author:
Liu Ping,Li Xiang,Zang Bin,Diao Guoyan
Abstract
AbstractWith the rapid advancement of sports analytics and fan engagement technologies, the volume and diversity of physique data generated by smart devices across various distributed sports platforms have grown significantly. Extracting insights and enhancing fan experiences from such data offer considerable benefits. Yet, this process unveils two primary challenges. Firstly, efficiently utilizing the vast datasets in sports analytics is daunting due to the complex nature of the sports industry. Secondly, the data collected from diverse sources and stored in distributed platforms contain sensitive information like fan preferences and athlete performance metrics, posing risks of privacy breaches. To address these challenges, we leverage an advanced Locality-Sensitive Hashing technique, known as PSDFP$$_{\text {ALSH}}$$
ALSH
, tailored for the sports domain. This paper presents a new privacy-preserving method for sports data fusion and prediction in distributed environments, utilizing enhanced Locality-Sensitive Hashing to protect sensitive information while maintaining high data utility. Through extensive experimentation, our approach demonstrates superior performance over existing methods in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and computational efficiency.
Publisher
Springer Science and Business Media LLC
Reference47 articles.
1. Dai H, Yu J, Li M, Wang W, Liu AX, Ma J, Qi L, Chen G (2023) Bloom filter with noisy coding framework for multi-set membership testing. IEEE Trans Knowl Data Eng 35(7):6710–6724
2. Gu R, Zhang K, Xu Z, Che Y, Fan B, Hou H, Dai H, Yi L, Ding Y, Chen G, et al (2022) Fluid: dataset abstraction and elastic acceleration for cloud-native deep learning training jobs. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp 2182–2195
3. Kong L, Li G, Rafique W, Shen S, He Q, Khosravi MR, Wang R, Qi L (2022) Time-aware missing healthcare data prediction based on arima model. IEEE/ACM Trans Comput Biol Bioinforma. https://doi.org/10.1109/TCBB.2022.3205064
4. Dai H, Wang X, Lin X, Gu R, Shi S, Liu Y, Dou W, Chen G (2021) Placing wireless chargers with limited mobility. IEEE Trans Mob Comput 22(6):3589–3603
5. Gu R, Chen Y, Liu S, Dai H, Chen G, Zhang K, Che Y, Huang Y (2021) Liquid: intelligent resource estimation and network-efficient scheduling for deep learning jobs on distributed GPU clusters. IEEE Trans Parallel Distrib Syst 33(11):2808–2820