Secure data sharing technology of medical privacy data in the Web 3.0 by Shusheng Guo, Cheng Chen, Qing Tong

“…Finally, data users apply to multiple parties for joint secure computing to obtain the use of private data. …”
Get full text

DPLQ: Location‐based service privacy protection scheme based on differential privacy by Qingyun Zhang, Xing Zhang, Mingyue Wang, Xiaohui Li

Get full text

Privacy guarantees for personal mobility data in humanitarian response by Nitin Kohli, Emily Aiken, Joshua E. Blumenstock

“…Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. …”
Get full text

Privacy protection method on time-series data publication by Dong YU, Hai-yan KANG

“…A differential privacy model was proposed based on the sampling filtering and the mechanism of evaluation.Firstly,fixed sampling method was used to sample the original data and the non-sampling data be published directly.Secondly,for the sampling date,utilize the differential privacy mechanism to add the noise.Then,use Kalman to correct the sampling date.Finally,use the mutual information to evaluate data under different sampling intervals.Through the experiment,it is proved that the mechanism can achieve a good balance between the practicality and protective.…”
Get full text

Synthetic data for privacy-preserving clinical risk prediction by Zhaozhi Qian, Thomas Callender, Bogdan Cebere, Sam M. Janes, Neal Navani, Mihaela van der Schaar

“…Compared with other privacy-enhancing approaches—such as federated learning—analyses performed on synthetic data can be applied downstream without modification, such that synthetic data can act in place of real data for a wide range of use cases. …”
Get full text

Proactive Data Categorization for Privacy in DevPrivOps by Catarina Silva, João P. Barraca, Paulo Salvador

“…PsDC is a data-categorization model designed for integration with the DevPrivOps methodology and for use in privacy-quantification models. …”
Get full text

Enhancing healthcare data privacy and interoperability with federated learning by Adil Akhmetov, Zohaib Latif, Benjamin Tyler, Adnan Yazici

“…Unlike traditional centralized learning (CL) solutions that require data centralization, our platform uses local model learning, which naturally improves data privacy. …”
Get full text

Aggregated privacy-preserving auditing for cloud data integrity by Kai HE, Chuan-he HUANG, Xiao-mao WANG, Jing WANG, Jiao-li SHI

“…To solve the problem of data integrity in cloud storage,an aggregated privacy-preserving auditing scheme was proposed.To preserve data privacy against the auditor,data proof and tag proof were encrypted and combined by using the bilinearity property of the bilinear pairing on the cloud server.Furthermore,an efficient index mechanism was designed to support dynamic auditing,which could ensure that data update operations did not lead to high additional computation or communication cost.Meanwhile,an aggregation method for different proofs was designed to handle multiple auditing requests.Thus the proposed scheme could also support batch auditing for multiple owners and multiple clouds and multiple files.The communication cost of batch auditing was independent of the number of auditing requests.The theoretical analysis and experimental results show that the proposed scheme is provably secure.Compared with existing auditing scheme,the efficacy of the proposed individual auditing and batch auditing improves 21.5% and 31.8% respectively.…”
Get full text

Differential Privacy and Collective Bargaining over Workplace Data by Sandy J.J. Gould

“…I argue that using differential privacy, a technique for processing data that makes it harder to determine who contributed data to a dataset, would remove an obstacle to employers sharing workplace data with worker representatives.…”
Get full text

Research on data integration privacy preservation mechanism for DaaS by Zhi-gang ZHOU, Hong-li ZHANG, Xiang-zhan YU, Pan-pan LI

“…In addition, the corres ding fake data set was used to assure the balanced distribution of data in each part, which realized privacy protection of data integration. …”
Get full text

Aggregated privacy-preserving auditing for cloud data integrity by Kai HE, Chuan-he HUANG, Xiao-mao WANG, Jing WANG, Jiao-li SHI

“…To solve the problem of data integrity in cloud storage,an aggregated privacy-preserving auditing scheme was proposed.To preserve data privacy against the auditor,data proof and tag proof were encrypted and combined by using the bilinearity property of the bilinear pairing on the cloud server.Furthermore,an efficient index mechanism was designed to support dynamic auditing,which could ensure that data update operations did not lead to high additional computation or communication cost.Meanwhile,an aggregation method for different proofs was designed to handle multiple auditing requests.Thus the proposed scheme could also support batch auditing for multiple owners and multiple clouds and multiple files.The communication cost of batch auditing was independent of the number of auditing requests.The theoretical analysis and experimental results show that the proposed scheme is provably secure.Compared with existing auditing scheme,the efficacy of the proposed individual auditing and batch auditing improves 21.5% and 31.8% respectively.…”
Get full text

Survey of artificial intelligence data security and privacy protection by Kui REN, Quanrun MENG, Shoukun YAN, Zhan QIN

“…Artificial intelligence and deep learning algorithms are developing rapidly.These emerging techniques have been widely used in audio and video recognition, natural language processing and other fields.However, in recent years, researchers have found that there are many security risks in the current mainstream artificial intelligence model, and these problems will limit the development of AI.Therefore, the data security and privacy protection was studied in AI.For data and privacy leakage, the model output based and model update based problem of data leakage were studied.In the model output based problem of data leakage, the principles and research status of model extraction attack, model inversion attack and membership inference attack were discussed.In the model update based problem of data leakage, how attackers steal private data in the process of distributed training was discussed.For data and privacy protection, three kinds of defense methods, namely model structure defense, information confusion defense and query control defense were studied.In summarize, the theoretical foundations, classic algorithms of data inference attack techniques were introduced.A few research efforts on the defense techniques were described in order to provoke further research efforts in this critical area.…”
Get full text

A Review of Differential Privacy in Individual Data Release by Jun Wang, Shubo Liu, Yongkai Li

“…The rapid development of mobile technology has improved users' quality of treatment, and tremendous amounts of medical information are readily available and widely used in data analysis and application, which bring on serious threats to users' privacy. …”
Get full text

INSURANCE DEVELOPMENTS IN THE LIGHT OF DATA USE by NADJIA, Madaoui

“…It aims to assess the extent to which existing legal regulations can safeguard policyholders from potential mistreatment resulting from the use of such methodologies. The study concludes that despite the safeguards offered by data and consumer protection laws, the unregulated and unconstrained application of data analytics and algorithms in risk evaluation could potentially harm policyholders by infringing on their privacy and leading to discrimination, thereby impinging on their rights.…”
Get full text

Protecting Privacy on Social Media: Mitigating Cyberbullying and Data Heist Through Regulated Use and Detox, with a Mediating Role of Privacy Safety Motivations by Jing Niu, Bilal Mazhar, Inam Ul Haq, Fatima Maqsood

“…Information theft and cyberbullying pose significant threats to users’ privacy on social media. This study applies Protection Motivation Theory (PMT) to explore how online information disclosure awareness and privacy concerns influence protective actions, such as regulated social media usage and detoxification, in response to negative experiences like data heist and cyberbullying. …”
Get full text

Privacy self-management and the issue of privacy externalities: of thwarted expectations, and harmful exploitation by Simeon de Brouwer

“…This term, related to similar concepts from the literature on privacy such as ‘networked privacy’ or ‘data pollution’, is used here to bring to light the incentives and exploitative dynamics behind a phenomenon which, I demonstrate, benefits both the user and the data controller to the detriment of third-party data subjects. …”
Get full text

Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD. by Yannick Marcon, Tom Bishop, Demetris Avraam, Xavier Escriba-Montagut, Patricia Ryser-Welch, Stuart Wheater, Paul Burton, Juan R González

“…DataSHIELD uses Opal which is a data integration system used by epidemiological studies and developed by the OBiBa open source project in the domain of bioinformatics. …”
Get full text

Enhancing privacy in clustering and data mining: A novel approach for sensitive data protection by Haythem Hayouni

“…However, these processes often involve the use of sensitive data, raising significant concerns about privacy, security, and trustworthiness. …”
Get full text

Right to privacy, Big Data and data protection: new challenges of the Colombian legal system by Dayron Dannylo Reyes Quintero, Margarita Rosa Lobo Contreras, Lucía Dayana Amaya Barbosa

“…Also, the perception and characteristics of Big Data are disclosed, trying to know how the rights involved in the materialization of the data analysis process can be protected; Finally, a critical sense is applied to the use of Big Data in modern political platforms, understanding the panorama for the Colombian legal system, which brings with it the probable violation of fundamental rights, such as personal privacy.…”
Get full text

Data structure and privacy protection analysis in big data environment based on blockchain technology by Yu Wang

Get full text