Efficient and privacy-preserving certificateless data aggregation in Internet of things–enabled smart grid by Aijing Sun, Axin Wu, Xiaokun Zheng, Fangyuan Ren

“…If the user’s electricity consumption is transmitted in plaintext, the data may be used by some illegal users. At the same time, malicious users may send false data such that the control center makes a wrong power resource scheduling. …”
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Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings by Rahul Haripriya, Nilay Khare, Manish Pandey

“…Abstract Ensuring data privacy in medical image classification is a critical challenge in healthcare, especially with the increasing reliance on AI-driven diagnostics. …”
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An explainable federated blockchain framework with privacy-preserving AI optimization for securing healthcare data by Tanisha Bhardwaj, K. Sumangali

“…Abstract With the rapid growth of healthcare data and the need for secure, interpretable, and decentralized machine learning systems, Federated Learning (FL) has emerged as a promising solution. …”
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Decoding privacy concerns: the role of perceived risk and benefits in personal health data disclosure by Havva Nur Atalay, Şebnem Yücel

“…Results The analysis revealed a significant negative relationship between individuals’ personal health data disclosure behaviour and their privacy concerns. …”
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Shuffled differential privacy protection method for K-Modes clustering data collection and publication by Weijin JIANG, Yilin CHEN, Yuqing HAN, Yuting WU, Wei ZHOU, Haijuan WANG

“…Aiming at the current problem of insufficient security in clustering data collection and publication, in order to protect user privacy and improve data quality in clustering data, a privacy protection method for K-Modes clustering data collection and publication was proposed without trusted third parties based on the shuffled differential privacy model.K-Modes clustering data collection algorithm was used to sample the user data and add noise, and then the initial order of the sampled data was disturbed by filling in the value domain random arrangement publishing algorithm.The malicious attacker couldn’t identify the target user according to the relationship between the user and the data, and then to reduce the interference of noise as much as possible a new centroid was calculated by cyclic iteration to complete the clustering.Finally, the privacy, feasibility and complexity of the above three methods were analyzed from the theoretical level, and the accuracy and entropy of the three real data sets were compared with the authoritative similar algorithms KM, DPLM and LDPKM in recent years to verify the effectiveness of the proposed model.The experimental results show that the privacy protection and data quality of the proposed method are superior to the current similar algorithms.…”
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A controllable privacy data transmission mechanism for Internet of things system based on blockchain by ZiXiang Nie, YuanZhenTai Long, SenLin Zhang, YueMing Lu

“…With the in-depth integration of traditional industries and information technology in Internet of things, wireless sensor networks are used more frequently to transmit the data generated from various application scenarios. …”
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Preserving Big Data Privacy in Cloud Environments Based on Homomorphic Encryption and Distributed Clustering by Shatha A. Baker

“…A partial homomorphic encryption system is used to encrypt data created by many sources or users and processed in the cloud without decrypting it, hence protecting data from attackers. …”
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Precision-Enhanced and Encryption-Mixed Privacy-Preserving Data Aggregation in Wireless Sensor Networks by Geng Yang, Sen Li, Xiaolong Xu, Hua Dai, Zhen Yang

“…Security is always a hot topic in wireless sensor networks (WSNs). Privacy-preserving data aggregation has emerged as an important concern in designing data aggregation algorithm. …”
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Time-based and privacy protection revocable and traceable data sharing scheme in cloud computing by Jiawei ZHANG, Jianfeng MA, Zhuo MA, Teng LI

“…General ciphertext-policy attribute-based encryption (CP-ABE) provides fine-grained access control for data sharing in cloud computing, but its plaintext formed access policy may cause leakage of private and sensitive data.And revoking a malicious user by accurately tracing the identity according to a leaked decryption key is a huge challenge.Moreover, most of existing revocable schemes incur long user revocation list and low efficiency.To solve these problems, a time-based and privacy preserving revocable and traceable data sharing scheme was proposed based on CP-ABE to support expressive monotonic and partial hidden access policy, large attribute universe by conceal the attribute values in access policy.Time-limited data access control using hierarchical identity-based encryption was achieved to set key valid period for users.Moreover, with the approaches of white-box tracing and binary tree, efficient user tracing and direct revocation with shorter revocation list was realized together with high efficiency via online/offline and verifiable outsourced decryption techniques.Furthermore, the scheme was secure under decisional q-BDHE assumption.Theoretical analysis and extensive experiments demonstrate its advantageous performance in computational and storage cost.…”
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A blockchain-based verifiable CP-ABE scheme for medical data privacy protection by Leilei Zhao, Guofang Dong, Hao Yuan

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Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework by Kamta Nath Mishra, Rajesh Kumar Lal, Paras Nath Barwal, Alok Mishra

“…Data privacy is a crucial concern for individuals using cloud storage services, and cloud service providers are increasingly focused on meeting this demand. …”
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South African Electoral Commission’s mobile app for voters: Data privacy and security dimensions by Nawal Omar, Scott Timcke

“…The analysis revealed several security and privacy concerns, including inadequately secured API keys, the potential for unauthorised access, and the potential for data breaches. …”
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Privacy Risk Assessment of Medical Big Data Based on Information Entropy and FCM Algorithm by Xiaoliang Zhang, Tianwei Guo

“…However, the high sensitivity and privacy of medical data also bring serious security challenges. …”
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DP-FedCMRS: Privacy-Preserving Federated Learning Algorithm to Solve Heterogeneous Data by Yang Zhang, Shigong Long, Guangyuan Liu, Junming Zhang

“…In federated learning, non-independently and non-identically distributed heterogeneous data on the clients can limit both the convergence speed and model utility of federated learning, and gradients can be used to infer original data, posing a threat to user privacy. …”
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A Blockchain-Based Secure Data Transaction and Privacy Preservation Scheme in IoT System by Jing Wu, Zeteng Bian, Hongmin Gao, Yuzhe Wang

“…With the explosive growth of Internet of Things (IoT) devices, massive amounts of heterogeneous data are continuously generated. However, IoT data transactions and sharing face multiple challenges such as limited device resources, untrustworthy network environment, highly sensitive user privacy, and serious data silos. …”
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Multi-party summation query method based on differential privacy by Xianmang HE

“…Differential privacy is considered to be a very reliable protection mechanism because it does not require the a prior knowledge for the attacker.However,differential privacy is rarely used in a multi-party environment.In view of this,the differential privacy is applied to the data summation query in multi-party environment.This method was described in detail and proved the security of the method.…”
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A decentralized privacy-preserving framework for diabetic retinopathy detection using federated learning and blockchain by Omar Dib

“…Diabetic Retinopathy (DR) detection in distributed telemedicine environments requires secure, scalable, and privacy-preserving solutions. Traditional federated learning (FL) relies on a central server, raising concerns about data privacy and system trust. …”
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Efficient and Privacy-Preserving Decision Tree Inference via Homomorphic Matrix Multiplication and Leaf Node Pruning by Satoshi Fukui, Lihua Wang, Seiichi Ozawa

“…Cloud computing is widely used by organizations and individuals to outsource computation and data storage. …”
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Privacy-preserving detection and classification of diabetic retinopathy using federated learning with FedDEO optimization by Dasari Bhulakshmi, Dharmendra Singh Rajput

“…FL enables collaborative learning across multiple decentralized devices while maintaining data privacy. FedDEO optimization enhances the model's performance by fine-tuning hyperparameters in a distributed manner. …”
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A differentially-private mechanism for multi-level data publishing by Wen-jing ZHANG, Hui LI

“…Privacy preserving technology had addressed the problem of privacy leakage during data publishing proc-ess,however,current data publishing technologies mostly focused on publishing privacy preserving data with single level,without considering some scenarios of multi-level users.Therefore,a differentially-private mechanism for multi-level data publishing was proposed.The proposed mechanism employed the Laplace mechanism with different privacy budgets to output results with different privacy protection levels.After the user’s level was determined ac-cording to the charge or privilege of that specific user,the goal that a user with high(low) level can only use the out-put result with low(high) privacy protection level which had low(high) error rate could be achieved.Finally,the evaluation results and security analysis show that our proposed framework can not only prevent from background knowledge attack,but also achieve multi-level data publishing with different error rates effectively .…”
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