Article
Engineering, Electrical & Electronic
Ting Bao, Lei Xu, Liehuang Zhu, Lihong Wang, Tielei Li
Summary: The development of vehicle positioning technologies has enabled in-vehicle recommendation systems, with one common form being the successive POI recommendation. This method helps users choose places to visit but raises privacy concerns due to raw check-in data collection. To address this issue, a recommendation framework using local differential privacy and three influence factors has been proposed to provide strong privacy protection for users.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Taolin Guo, Shunshun Peng, Yong Li, Mingliang Zhou, Trieu-Kien Truong
Summary: Social recommendation is a recommendation technology that utilizes social relations to improve merchandise sales and user satisfaction. This paper proposes a novel locally differentially private social recommendation method, which perturbs user degrees within a given interval to discover coarse-grained communities and generates a fine-grained social graph based on intra-community and inter-community relations, ensuring recommendation accuracy.
INFORMATION SCIENCES
(2023)
Article
Chemistry, Multidisciplinary
Xia Wu, Lei Xu, Liehuang Zhu
Summary: In this paper, an LDP-based federated learning framework is proposed to meet the personalized privacy requirements of clients, considering both IID and non-IID datasets. Model perturbation methods and model aggregation methods are designed. Experimental results demonstrate the effectiveness of the proposed methods in the personalized privacy preserving scenario.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Multidisciplinary
Mingyue Zhang, Junlong Zhou, Gongxuan Zhang, Lei Cui, Tian Gao, Shui Yu
Summary: This paper presents a fine-grained personalized differential privacy data publishing scheme (APDP) for social networks. The scheme defines privacy protection levels based on attribute values and maps them to the amount of noise required to add using the TOPSIS method. Additionally, access control is integrated with differential privacy to prevent illegal data downloads. Theoretical analysis and simulations demonstrate that APDP achieves efficient personalized differential privacy data publishing with reasonable data utility.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Chenxu Wang, Yu Yang, Kaiqiang Suo, Pinghui Wang
Summary: Learning user preferences from implicit feedback through collaborative ranking has gained increasing attention. This paper proposes a novel setwise ranking model that considers users' preference rankings among multiple sets of items. The model exploits users' behavioral similarities to mine potential preference items and outperforms state-of-the-art methods according to experimental results.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Chonghuan Xu, Xinyao Mei, Dongsheng Liu, Kaidi Zhao, Austin Shijun Ding
Summary: This paper proposes a hybrid POI recommendation model based on local differential privacy, to address the issues of privacy preservation and insufficient response ability in utilizing multiple types of user information for recommendation. The model incorporates randomized response techniques and a virtual check-in time generation method to distort user ratings and social relationships, and combines three sub-models to consider user preferences, social relationships, check-in trajectories, geographical correlation, and POI categories for recommendation. Experimental results demonstrate the superiority of the proposed method over existing approaches.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Information Systems
Jianhao Wei, Yaping Lin, Xin Yao, Jin Zhang, Xinbo Liu
Summary: This article introduces a genetic matching scheme based on differential privacy technology to protect genetic data privacy and achieve effective genetic matching. The scheme constructs noisy published and query sequences using differential privacy algorithms, and calculates the longest common subsequence through a dynamic programming algorithm to achieve matching results.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2021)
Article
Chemistry, Multidisciplinary
Bin Cheng, Ping Chen, Xin Zhang, Keyu Fang, Xiaoli Qin, Wei Liu
Summary: With the rapid development of ubiquitous data collection and analysis, data privacy in recommended systems is facing challenges. Differential privacy technology can protect privacy but introduces unwanted noise. Considering personalized requirements, a collaborative filtering algorithm is proposed to reduce unwanted noise and protect privacy. Experimental results show improved recommendation performance and privacy protection.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Junmei Feng, Kunwei Wang, Qiguang Miao, Yue Xi, Zhaoqiang Xia
Summary: This paper proposes a hybrid-feedback collaborative filtering model that addresses the absence problem of negative feedback in the Bayesian personalized ranking (BPR) model by jointly exploiting explicit and implicit feedback. The model successfully extracts both implicit and explicit feedback features, and achieves competitive performance on public datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Xu Yang, Ziyi Huan, Yisong Zhai, Ting Lin
Summary: This research focuses on personalized recommendation based on knowledge graphs, including constructing knowledge graphs, improving the TransE algorithm, and combining ranking learning and neural networks to build two recommendation models. Experimental results demonstrate that these models effectively enhance recommendation accuracy and recall.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Guangli Li, Jianwu Zhuo, Chuanxiu Li, Jin Hua, Tian Yuan, Zhengyu Niu, Donghong Ji, Renzhong Wu, Hongbin Zhang
Summary: The study introduces a new multi-modal visual adversarial Bayesian personalized ranking (MVABPR) model to address the challenge of data sparseness in recommendation systems. By leveraging cross-modal semantic features and employing an adversarial learning strategy, the MVABPR model proves to be effective and robust, outperforming competitive baselines. Additionally, the model is capable of jointly learning visual information and user ratings, while accurately capturing the implicit feeling tone of recommended items on large-scale sparser datasets.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Arpita Chaudhuri, Monalisa Sarma, Debasis Samanta
Summary: The extraneous growth of scientific information over the Internet has made it difficult for researchers to find relevant papers among millions of research papers. Existing research paper recommendation systems have limitations in exploiting prominent information of papers and do not consider a sound ranking strategy. This study proposes a systematic hidden attribute-based recommendation engine (SHARE) that utilizes multiple hidden features to provide valuable insights of papers and a novel ranking strategy to retrieve personalized and important papers.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Runqing Jiang, Shanshan Feng, Shoujia Zhang, Xi Li, Yan Yao, Huaxiang Zhang
Summary: To improve the performance of recommender systems, this paper proposes an Assembled Collaborative Ranking with Random Walk (ACR-RW) approach that combines collaborative ranking and random walk methods. The approach ranks items based on absolute and relative correlative information, in order to recommend top-ranked items to potentially interested users.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Physics, Multidisciplinary
Xiaoying Shen, Hang Jiang, Yange Chen, Baocang Wang, Le Gao
Summary: This paper introduces a perturbation algorithm (PDPM) that satisfies personalized local differential privacy (PLDP), resolving the issue of inadequate or excessive privacy protection for some participants due to the same privacy budget set for all clients.
Article
Computer Science, Hardware & Architecture
Xiaoguang Li, Haonan Yan, Zelei Cheng, Wenhai Sun, Hui Li
Summary: This research proposes a novel personalized local differential privacy mechanism to defend against the equation-solving model extraction attack. By adding high-dimensional Gaussian noise to the model coefficients, the model is obfuscated. Experimental results show that the proposed mechanism outperforms existing differential-privacy-enabled solutions.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Computer Science, Information Systems
Bing Chang, Yao Cheng, Bo Chen, Fengwei Zhang, Wen-Tao Zhu, Yingjiu Li, Zhan Wang
COMPUTERS & SECURITY
(2018)
Proceedings Paper
Computer Science, Theory & Methods
Jinghui Liao, Bo Chen, Weisong Shi
Summary: Modern mobile devices are used to store and process sensitive information, and a new PDE system has been proposed to protect the data and device owners. This system isolates hidden and public data, and incorporates deniability features into ARM TrustZone for immunity to side-channel attacks.
2021 ACM/IEEE 6TH SYMPOSIUM ON EDGE COMPUTING (SEC 2021)
(2021)
Proceedings Paper
Computer Science, Information Systems
Shashank Reddy Danda, Xiaoyong Yuan, Bo Chen
Summary: This work investigates model stealing attacks on deep neural networks running on mobile devices for the first time, confirming the feasibility of stealing DNN models with high accuracy and small overhead from mobile devices.
SECURITY AND PRIVACY IN COMMUNICATION NETWORKS, SECURECOMM 2021, PT II
(2021)
Proceedings Paper
Computer Science, Information Systems
Niusen Chen, Wen Xie, Bo Chen
Summary: mobiDOM is proposed to securely detect OS-level malware in mobile computing devices by integrating a malware detector in the flash translation layer and building a trusted application in the Arm TrustZone secure world to communicate covertly. Security analysis and experiments have shown that mobiDOM can effectively detect OS-level malware securely.
APPLIED CRYPTOGRAPHY AND NETWORK SECURITY WORKSHOPS, ACNS 2021
(2021)
Article
Computer Science, Information Systems
Weijing You, Lei Lei, Bo Chen, Limin Liu
Summary: Deduplication can significantly reduce storage cost by storing unique copies of duplicate data. However, when combined with confidentiality, encryption may complicate deduplication. The Message-Locked Encryption (MLE) is utilized to ensure deduplicability even after encryption by different data owners. Re-encrypting outsourced data is crucial for continuous confidentiality, and the SEDER system addresses this by leveraging AONT, designing DRE, and proposing PoWC. Security analysis and experimental evaluation validate the security and efficiency of SEDER.
Proceedings Paper
Computer Science, Information Systems
Biao Gao, Bo Chen, Shijie Jia, Luning Xia
PROCEEDINGS OF THE 2019 ACM ASIA CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (ASIACCS '19)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, Yujun Zhang
THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
(2018)
Proceedings Paper
Computer Science, Theory & Methods
Bing Chang, Fengwei Zhang, Bo Chen, Yingjiu Li, Wen-Tao Zhu, Yangguang Tian, Zhan Wang, Albert Ching
2018 48TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN)
(2018)
Proceedings Paper
Computer Science, Information Systems
Shijie Jia, Luning Xia, Bo Chen, Peng Liu
CCS'17: PROCEEDINGS OF THE 2017 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY
(2017)
Article
Computer Science, Information Systems
Bo Chen, Reza Curtmola
JOURNAL OF COMPUTER SECURITY
(2017)
Proceedings Paper
Computer Science, Information Systems
Le Guan, Shijie Jia, Bo Chen, Fengwei Zhang, Bo Luo, Jingqiang Lin, Peng Liu, Xinyu Xing, Luning Xia
33RD ANNUAL COMPUTER SECURITY APPLICATIONS CONFERENCE (ACSAC 2017)
(2017)
Proceedings Paper
Computer Science, Information Systems
Xuying Meng, Zhiwei Xu, Bo Chen, Yujun Zhang
2016 IEEE TRUSTCOM/BIGDATASE/ISPA
(2016)
Proceedings Paper
Computer Science, Information Systems
Danye Wu, Zhiwei Xu, Bo Chen, Yujun Zhang
2016 IEEE TRUSTCOM/BIGDATASE/ISPA
(2016)
Article
Computer Science, Information Systems
Qionglu Zhang, Shijie Jia, Bing Chang, Bo Chen