Blockchain data sharing scheme based on localized difference privacy and attribute-based searchable encryption by Tao FENG, Liqiu CHEN, Junli FANG, Jianming SHI

“…Aiming at the problem that traditional cloud-based data sharing schemes rely on trusted third parties and only focus on data privacy protection or access control, a blockchain data sharing scheme based on localized difference privacy and attribute-based searchable encryption was proposed.The blockchain and cloud server were combined to store data chain by chain and provide efficient, reliable and tamper-proof data sharing.Firstly, the localization difference privacy was introduced to preprocess the shared data to protect the privacy of the data owner and resist the attack of the untrusted third party.Secondly, the searchable encryption technology and attribute-based encryption were combined to realize data privacy protection, support ciphertext retrieval, and provide fine-grained access control for shared data.Finally, the safety, the correctness proof, and the experimental analysis proves that the proposed scheme meets the safety objectives.…”
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Blockchain data sharing scheme based on localized difference privacy and attribute-based searchable encryption by Tao FENG, Liqiu CHEN, Junli FANG, Jianming SHI

“…Aiming at the problem that traditional cloud-based data sharing schemes rely on trusted third parties and only focus on data privacy protection or access control, a blockchain data sharing scheme based on localized difference privacy and attribute-based searchable encryption was proposed.The blockchain and cloud server were combined to store data chain by chain and provide efficient, reliable and tamper-proof data sharing.Firstly, the localization difference privacy was introduced to preprocess the shared data to protect the privacy of the data owner and resist the attack of the untrusted third party.Secondly, the searchable encryption technology and attribute-based encryption were combined to realize data privacy protection, support ciphertext retrieval, and provide fine-grained access control for shared data.Finally, the safety, the correctness proof, and the experimental analysis proves that the proposed scheme meets the safety objectives.…”
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A high-dimensional numeric data collection algorithm for local difference privacy based on random projection by Huizhong SUN, Jianyu YANG, Xiang CHENG, Sen SU

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Adaptive personalized privacy-preserving data collection scheme with local differential privacy by Haina Song, Hua Shen, Nan Zhao, Zhangqing He, Wei Xiong, Minghu Wu, Mingwu Zhang

“…Local differential privacy (LDP) is a state-of-the-art privacy notion that enables terminal participants to share their private data safely while controlling the privacy disclosure at the source. …”
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Enhancing Privacy in Lightweight Data Encoding for Sensitive Applications by Yan Wang, Min Xu, Sheng Zhong

“…We also introduce the Utility-optimized Bloom Filter (UBF), a privacy-preserving mechanism grounded in User-Level Local Differential Privacy (ULDP), which offers formal and quantifiable privacy guarantees. …”
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Privacy-Preserving Federated Class-Incremental Learning by Jue Xiao, Xueming Tang, Songfeng Lu

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IFed: A novel federated learning framework for local differential privacy in Power Internet of Things by Hui Cao, Shubo Liu, Renfang Zhao, Xingxing Xiong

“…Last, users were categorized based on different levels of privacy requirements, and stronger privacy guarantee was provided for sensitive users. …”
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Employing Ontology-Alignment and Locality-Sensitive Hashing to Improve Attribute Interoperability in Federated eID Systems by Walter Priesnitz Filho, Carlos Ribeiro, Thomas Zefferer

“…Furthermore, providing attributes to intermediate entities required to align and aggregate attributes potentially violates privacy rules. To tackle these problems, we propose the use of combined ontology-alignment (OA) approaches and locality-sensitive hashing (LSH) functions. …”
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Reversible Data Hiding Algorithm in Encrypted Images with Difference Encoding and Block Compression by Guoqing GE, Bin GE, Chenxing XIA, Zhimeng WANG

“…With the development of digital information and cloud-computing technology toward maturity, reversible hiding of data in encrypted images is gradually becoming an area of intensive research for protection of data privacy in communications. Nonetheless, notable challenges still exist regarding achieving greater compression of carriers to enhance embedding capacity by adaptive-recognition coding of pixels in local areas based on the distribution characteristics of pixels in different cover images. …”
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Novel Bounds for Incremental Hessian Estimation With Application to Zeroth-Order Federated Learning by Alessio Maritan, Luca Schenato, Subhrakanti Dey

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Quantization-based chained privacy-preserving federated learning by Ya Liu, Shumin Wu, Yibo Li, Fengyu Zhao, Yanli Ren

“…Abstract Federated Learning (FL) is an advanced distributed machine learning framework crucial in protecting data privacy and security. By enabling multiple participants to train models while keeping their data local collaboratively, FL effectively mitigates the risks associated with centralized storage and sharing of raw data. …”
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Privacy-preserving federated learning framework with dynamic weight aggregation by Zuobin YING, Yichen FANG, Yiwen ZHANG

“…There are two problems with the privacy-preserving federal learning framework under an unreliable central server.① A fixed weight, typically the size of each participant’s dataset, is used when aggregating distributed learning models on the central server.However, different participants have non-independent and homogeneously distributed data, then setting fixed aggregation weights would prevent the global model from achieving optimal utility.② Existing frameworks are built on the assumption that the central server is honest, and do not consider the problem of data privacy leakage of participants due to the untrustworthiness of the central server.To address the above issues, based on the popular DP-FedAvg algorithm, a privacy-preserving federated learning DP-DFL algorithm for dynamic weight aggregation under a non-trusted central server was proposed which set a dynamic model aggregation weight.The proposed algorithm learned the model aggregation weight in federated learning directly from the data of different participants, and thus it is applicable to non-independent homogeneously distributed data environment.In addition, the privacy of model parameters was protected using noise in the local model privacy protection phase, which satisfied the untrustworthy central server setting and thus reduced the risk of privacy leakage in the upload of model parameters from local participants.Experiments on dataset CIFAR-10 demonstrate that the DP-DFL algorithm not only provides local privacy guarantees, but also achieves higher accuracy rates with an average accuracy improvement of 2.09% compared to the DP-FedAvg algorithm models.…”
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Fine-grained privacy operation control method for layout documents by Peijie YIN, Fenghua LI, Ben NIU, Haiyang LUO, Bin KUANG, Lingcui ZHANG

“…In view of the problem of privacy information disclosure caused by uncontrolled forwarding in the context of frequent exchange of privacy information, a fine-grained privacy operation control method for layout documents was proposed, which could achieve differentiated fine-grained privacy operation control according to the requirements of the sharer during the sharing process of privacy information.For the received multimodal layout document, the existing privacy operation control strategy was extracted, which combined the current sharer’s use attribute and the receiver’s privacy protection ability and other factors.The privacy operation control strategy was generated iteratively, and an abstract control strategy generation algorithm framework was given.Based on the iterative privacy operation control strategy and combined with specific operation scenarios, the differentiated data-masking control, exchange boundary control and local use control were carried out for different modes of information components, and the abstract privacy operation control algorithm framework was given.A prototype system for privacy operation control of OFD (open fixed-layout document) was developed to verify the above algorithms.The generation and delivery of iterative privacy operation control strategy based on friendship, as well as the differential data-masking control, exchange boundary control and local use control of OFD were implemented in the instant messaging system.…”
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A Novel Approach for Differential Privacy-Preserving Federated Learning by Anis Elgabli, Wessam Mesbah

“…Our analysis reveals that, as far as only one local iteration is performed by each client before transmitting to the parameter server (PS) for FedGD, both FedGD and FedAvg achieve the same accuracy/loss for the same privacy guarantees, despite requiring different perturbation noise power. …”
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Analysis of security and privacy challenges of smart health and sensing systems by Vimal Bibhu, Anand Kumar Shukla, Basu Dev Shivahare, Jatinder Kaur, Mohd Shukri, Ab Yajid Johar MGM, Ayham Sameer Faaq, Mohammed Kareem AlShujairi, Shatrudhan Pandey

“…However, one of the major challenges faced by this system is the security and privacy of patient health data, which is considered vulnerable to local intruders and cyber attackers. …”
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A Survey on Secure WiFi Sensing Technology: Attacks and Defenses by Xingyu Liu, Xin Meng, Hancong Duan, Ze Hu, Min Wang

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Review of privacy computing techniques for multi-party data fusion analysis by LIU Shenglong, HUANG Xiuli, JIANG Yiwen, JIANG Jiawei, TIAN Yuechi, ZHOU Zejun, NIU Ben

“…In terms of multi-party privacy information sharing control, localized control, extended control, and anonymization control techniques were discussed. …”
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A multimodal differential privacy framework based on fusion representation learning by Chaoxin Cai, Yingpeng Sang, Hui Tian

“…Then based on this representation, we use the Local Differential Privacy (LDP) mechanism to protect data. …”
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Hidden Anchor: A Lightweight Approach for Physical Layer Location Privacy by Rania El-Badry, Moustafa Youssef, Ahmed Sultan

“…In this paper, we propose Hidden Anchor, an algorithm that provides anchor physical layer location privacy for different classes of localization algorithms. …”
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FDAE: Lightweight Privacy Protection Based on Face Detection and Image Encryption by Li You, Qianqian Liu, Shuai Li, Tianyu Yang

“…Moreover, the decrypted images exhibit negligible differences from the original ones, making FDAE well-suited for batch image processing tasks and practical privacy protection applications.…”
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