Yuanmin Shi^1,2, Siran Yin^1,2, Ze Chen^1,2, Leiming Yan^1,2,*

Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 9-16, 2021, DOI:10.32604/jihpp.2021.012193

Abstract Privacy protection is a hot research topic in information security field. An improved XGBoost algorithm is proposed to protect the privacy in classification tasks. By combining with differential privacy protection, the XGBoost can improve the classification accuracy while protecting privacy information. When using CART regression tree to build a single decision tree, noise is added according to Laplace mechanism. Compared with random forest algorithm, this algorithm can reduce computation cost and prevent overfitting to a certain extent. The experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the privacy information in training data. More >

Kang-Cheng Chen¹, Chia-Mu Yu^2,*, Tooska Dargahi³

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 761-787, 2021, DOI:10.32604/cmc.2021.014984

Abstract The advancement of information technology has improved the delivery of financial services by the introduction of Financial Technology (FinTech). To enhance their customer satisfaction, Fintech companies leverage artificial intelligence (AI) to collect fine-grained data about individuals, which enables them to provide more intelligent and customized services. However, although visions thereof promise to make customers’ lives easier, they also raise major security and privacy concerns for their users. Differential privacy (DP) is a common privacy-preserving data publishing technique that is proved to ensure a high level of privacy preservation. However, an important concern arises from the trade-off between the data utility… More >

Hongyu Chen¹, Shuyu Li^{1, *}, Zhaosheng Zhang¹

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2665-2685, 2020, DOI:10.32604/cmc.2020.010965

Abstract In recent years, mobile Internet technology and location based services have wide application. Application providers and users have accumulated huge amount of trajectory data. While publishing and analyzing user trajectory data have brought great convenience for people, the disclosure risks of user privacy caused by the trajectory data publishing are also becoming more and more prominent. Traditional k-anonymous trajectory data publishing technologies cannot effectively protect user privacy against attackers with strong background knowledge. For privacy preserving trajectory data publishing, we propose a differential privacy based (k-Ψ)-anonymity method to defend against re-identification and probabilistic inference attack. The proposed method is divided… More >

Haijiang Liu¹, Lianwei Cui², Xuebin Ma^{1, *}, Celimuge Wu³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 369-385, 2020, DOI:10.32604/cmc.2020.010987

Abstract Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications. However, users’ personal privacy will be leaked in the mining process. In recent years, application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method. Local differential privacy means that users first perturb the original data and then send these data to the aggregator, preventing the aggregator from revealing the user’s private information. We propose a novel framework that implements frequent itemset mining under local differential privacy and is applicable to user’s… More >

Wenxi Han^{1, 2}, Mingzhi Cheng^{3, *}, Min Lei^{1, 2}, Hanwen Xu², Yu Yang^{1, 2}, Lei Qian⁴

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1025-1038, 2020, DOI:10.32604/cmc.2020.09815

Abstract In recent years, with the continuous advancement of the intelligent process of the Internet of Vehicles (IoV), the problem of privacy leakage in IoV has become increasingly prominent. The research on the privacy protection of the IoV has become the focus of the society. This paper analyzes the advantages and disadvantages of the existing location privacy protection system structure and algorithms, proposes a privacy protection system structure based on untrusted data collection server, and designs a vehicle location acquisition algorithm based on a local differential privacy and game model. The algorithm first meshes the road network space. Then, the dynamic… More >

Shuyu Li¹, Guozheng Zhang^{1, *}

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 223-241, 2020, DOI:10.32604/cmc.2020.07499

Abstract With the popularity of sensor-rich mobile devices, mobile crowdsensing (MCS) has emerged as an effective method for data collection and processing. However, MCS platform usually need workers’ precise locations for optimal task execution and collect sensing data from workers, which raises severe concerns of privacy leakage. Trying to preserve workers’ location and sensing data from the untrusted MCS platform, a differentially private data aggregation method based on worker partition and location obfuscation (DP-DAWL method) is proposed in the paper. DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group… More >

Mengwei Hou¹, Rong Wei^1,*, Tiangang Wang¹, Yu Cheng², Buyue Qian³

CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 137-149, 2018, DOI: 10.3970/cmc.2018.02438

Abstract Collaborative filtering (CF) methods are widely adopted by existing medical recommendation systems, which can help clinicians perform their work by seeking and recommending appropriate medical advice. However, privacy issue arises in this process as sensitive patient private data are collected by the recommendation server. Recently proposed privacy-preserving collaborative filtering methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Privacy Preserving Medical Recommendation (PPMR) algorithm, which can protect patients’ treatment information and demographic… More >

Lan Sun¹, Chen Ge¹, Xin Huang¹, Yingjie Wu^1,*, Yan Gao²

CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 61-78, 2019, DOI:10.32604/cmc.2019.03744

Abstract Continuous response of range query on steaming data provides useful information for many practical applications as well as the risk of privacy disclosure. The existing research on differential privacy streaming data publication mostly pay close attention to boosting query accuracy, but pay less attention to query efficiency, and ignore the effect of timeliness on data weight. In this paper, we propose an effective algorithm of differential privacy streaming data publication under exponential decay mode. Firstly, by introducing the Fenwick tree to divide and reorganize data items in the stream, we achieve a constant time complexity for inserting a new item… More >