Privacy computing:concept, connotation and its research trend by Feng-hua LI, Hui LI, Yan JIA, Neng-hai YU, Jian WENG

“…s: With the widespread application of mobile Internet, cloud computing and big data technologies, people enjoy the convenience of electronic business, information retrieval, social network and other services, whereas the threats of privacy leaks are ever growing due to the use of big data analytics. …”
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Providing Privacy Protection and Personalization Awareness for Android Devices by Hongliang Liang, Dongyang Wu, Shirun Liu, Hao Dai, Haifeng Liu

“…As a result, the risk of users compromising their privacy has risen exponentially. Mobile users currently cannot control how various applications handle the privacy of their sensor data. …”
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A Survey on Privacy-Preserving Machine Learning Inference by Stanisław Barański

“…Use cases in healthcare, finance, and education show how these techniques balance privacy with practical performance. …”
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Adaptive Transformation for Robust Privacy Protection in Video Surveillance by Mukesh Saini, Pradeep K. Atrey, Sharad Mehrotra, Mohan Kankanhalli

“…However, the current detectors are not fully reliable, leading to breaches in privacy protection. In this paper, we propose a privacy protection method that adopts adaptive data transformation involving the use of selective obfuscation and global operations to provide robust privacy even with unreliable detectors. …”
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Survey on vertical federated learning: algorithm, privacy and security by Jinyin CHEN, Rongchang LI, Guohan HUANG, Tao LIU, Haibin ZHENG, Yao CHENG

“…., model parameters, parameter gradients, embedding representation, etc.） applied to data distributed across various institutions.FL reduces the risk of privacy leakage, since raw data is not allowed to leave the institution.According to the difference in data distribution between institutions, FL is usually divided into horizontal federated learning （HFL）, vertical federated learning （VFL）, and federal transfer learning （TFL）.VFL is suitable for scenarios where institutions have the same sample space but different feature spaces and is widely used in fields such as medical diagnosis, financial and security of VFL.Although VFL performs well in real-world applications, it still faces many privacy and security challenges.To the best of our knowledge, no comprehensive survey has been conducted on privacy and security methods.The existing VFL was analyzed from four perspectives: the basic framework, communication mechanism, alignment mechanism, and label processing mechanism.Then the privacy and security risks faced by VFL and the related defense methods were introduced and analyzed.Additionally, the common data sets and indicators suitable for VFL and platform framework were presented.Considering the existing challenges and problems, the future direction and development trend of VFL were outlined, to provide a reference for the theoretical research of building an efficient, robust and safe VFL.…”
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Sensitivity-Aware Differential Privacy for Federated Medical Imaging by Lele Zheng, Yang Cao, Masatoshi Yoshikawa, Yulong Shen, Essam A. Rashed, Kenjiro Taura, Shouhei Hanaoka, Tao Zhang

“…Our idea is that the sensitivity of each data sample can be objectively measured using real-world attacks. …”
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Federated Learning for Privacy-Preserving Employee Performance Analytics by Jay Barach

“…This paper introduces HFAN-Priv, a hierarchical federated attention network designed to predict employee resignation risk and evaluate performance trends without sharing raw data across organizations. The framework integrates feature-level and instance-level attention to model complex workforce patterns, applies differential privacy through gradient masking to ensure compliance with data protection regulations, and enhances interpretability using local SHAP and LIME explanations. …”
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Customer adoption of smartwatches – a privacy calculus perspective by Ajay Kumar, Parvez Ahmad, Daruri Venkata Srinivas Kumar, Megha Gupta

“…The authors collected 310 responses using a structured questionnaire; after data cleaning, 270 responses were used for data analysis. …”
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DEBPIR: enhancing information privacy in decentralized business modeling by Gulshan Kumar, Rahul Saha, Mauro Conti, Tai Hoon Kim

“…Abstract Business modelling often involves extensive data collection and analysis, raising concerns about privacy infringement. …”
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Privacy for Process Mining: A Systematic Literature Review by Ibrahim Ileri, Tugba Gurgen Erdogan, Ayca Kolukisa-Tarhan

“…However, privacy preservation issues arise when handling such data. …”
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Information Security and Privacy Management in Intelligent Transportation Systems by Mariia Bakhtina, Raimundas Matulevičius, Lukaš Malina

“…First, the framework is used to extract data during the literature review defining state-of-the-art aspects and measures. …”
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Expedite Privacy-Preserving Emergency Communication Scheme for VANETs by Long Chen, Xuedan Jia, Lixia Meng, Liangmin Wang

“…But requirements of information collection and data transmission in emergency scenario are very imperative. …”
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Examining the applicability of hard data protection law on demographically identifiable information (DII): the case of humanitarian UAV/drone images in Malawi by Rogers Alunge Alunge Nnangsope

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Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward by Sudeep Tanwar, Qasim Bhatia, Pruthvi Patel, Aparna Kumari, Pradeep Kumar Singh, Wei-Chiang Hong

“…The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. …”
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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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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 Comprehensive Review of Cryptographic Techniques in Federated Learning for Secure Data Sharing and Applications by Anik Sen, Swee-Huay Heng, Shing-Chiang Tan

“…Federated Learning (FL) introduces a decentralised machine learning paradigm whereby models can be trained over distributed nodes without sharing data. Despite its promise, FL faces significant security challenges, such as gradient inversion, model poisoning, and privacy leakage, which involve strong cryptographic techniques. …”
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The Nature of the Right to Personal Data: A Civil Law Perspective by L. V. Shvets

“…This paper aims to examine the nature of the right to personal data. Based on an analysis of Russian legislation, the Author concludes that there exists an unnamed, independent subjective right, which serves to enable the data subject to control and define the conditions for data processing, as well as to protect personal data from unauthorized use. …”
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Practical and ready-to-use methodology to assess the re-identification risk in anonymized datasets by Louis Philippe Sondeck, Maryline Laurent

“…This paper proposes a practical and ready-to-use methodology for re-identification risk assessment, the originality of which is manifold: (1) it is the first to follow well-known risk analysis methods (e.g. …”
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Trajectory differential privacy protection mechanism based on prediction and sliding window by Ayong YE, Lingyu MENG, Ziwen ZHAO, Yiqing DIAO, Jiaomei ZHANG

“…To address the issues of privacy budget and quality of service in trajectory differential privacy protection,a trajectory differential privacy mechanism integrating prediction disturbance was proposed.Firstly,Markov chain and exponential perturbation method were used to predict the location which satisfies the differential privacy and temporal and spatial security,and service similarity map was introduced to detect the availability of the location.If the prediction was successful,the prediction location was directly used to replace the location of differential disturbance,to reduce the privacy cost of continuous query and improve the quality of service.Based on this,the trajectory privacy budget allocation mechanism based on w sliding window was designed to ensure that any continuous w queries in the trajectory meet the ε-differential privacy and solve the trajectory privacy problem of continuous queries.In addition,a privacy customization strategy was designed based on the sensitivity map.By customizing the privacy sensitivity of semantic location,the privacy budget could be customized to improve its utilization.Finally,the validity of the scheme was verified by real data set experiment.The results illustrate that it offers the better privacy and quality of service.…”
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