Privacy Preserving Data Collection (original) (raw)
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Privacy preserving health data processing
2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom), 2014
The usage of electronic health data from different sources for statistical analysis requires a toolset where the legal, security and privacy concerns have been taken into consideration. The health data are typically located at different general practices and hospitals. The data analysis consists of local processing at these locations, and the locations become nodes in a computing graph. To support the legal, security and privacy concerns, the proposed toolset for statistical analysis of health data uses a combination of secure multi-party computation (SMC) algorithms, symmetric and public key encryption, and public key infrastructure (PKI) with certificates and a certificate authority (CA). The proposed toolset should cover a wide range of data analysis with different data distributions. To achieve this, large set of possible SMC algorithms and computing graphs have to be supported.
Analytical Approach for Privacy Preserving of Medical Data
E-Science is getting more collaborative and distributed hence the data privacy of these digitalized big data is a important task, especially when the data is personal, confidential and contains sensitive information like patient's mental health records, psychotherapy notes and human behavior records etc. Medical health records are integral to self managed healthcare are applicable but when it comes to patient access it become a serious concern that requires a better balance for personalization, information care, security controls and privacy protection of individual data. For privacy management, existing policies are too restrictive and there is not much privacy aware data analysis research, for supporting of big data analysis. Thus there is need of analyzing the large variety, variation, velocity and volume of data. However, increased accessibility of highly sensitive mental records threatens the privacy and confidentiality of patient's records. In this paper we are describing the data analysis processes as workflows for medical data. We describe the workflow for privacy aware data analysis in mental health research and develop a analytical approach for privacy preserving of medical data to address these concerns.
An efficient privacy mechanism for electronic health records
Computers & Security, 2018
He completed his PHD with distinction in the year 2013. His area of research is data privacy using artificial intelligence techniques. He has several publications in international journals. He is also the author of a book on data privacy. He serves in the technical program committees of various international conferences and journals.
IJERT-Analytical Approach for Privacy Preserving of Medical Data
International Journal of Engineering Research and Technology (IJERT), 2015
https://www.ijert.org/analytical-approach-for-privacy-preserving-of-medical-data https://www.ijert.org/research/analytical-approach-for-privacy-preserving-of-medical-data-IJERTV4IS100466.pdf E-Science is getting more collaborative and distributed hence the data privacy of these digitalized big data is a important task, especially when the data is personal, confidential and contains sensitive information like patient's mental health records, psychotherapy notes and human behavior records etc. Medical health records are integral to self managed healthcare are applicable but when it comes to patient access it become a serious concern that requires a better balance for personalization, information care, security controls and privacy protection of individual data. For privacy management, existing policies are too restrictive and there is not much privacy aware data analysis research, for supporting of big data analysis. Thus there is need of analyzing the large variety, variation, velocity and volume of data. However, increased accessibility of highly sensitive mental records threatens the privacy and confidentiality of patient's records. In this paper we are describing the data analysis processes as workflows for medical data. We describe the workflow for privacy aware data analysis in mental health research and develop a analytical approach for privacy preserving of medical data to address these concerns.
The modeling of privacy preserving and statistically analysable database (PPSADB) system
International Journal of Advanced Computer Research
Nowadays health information (HI) is digitized and stored in health record system such as electronic health record (EHR), electronic medical record (EMR) or personal health record (PHR) systems. Some people manage their health through remote medical system. Some people want to share their symptoms or experiences (success or fail) with other people and researchers find better treatments through on-line website service systems ("PatientsLikeMe"[1] or "Curetogether"[2]). Recently, privacy has gotten into hot issues again as one of the most important and necessary problems because the general data protection regulation (GDPR) began to replace the previous data protection directive of the European Union (EU) from May 25, 2018. The important thing is that HI has bilateral features. Health data include a lot of sensitive and private things, while they are encouraged to contribute to medical research. Hence, HI systems should satisfy both properties of privacy protection and medical research data as a publishable database (DB) systems.
Distributed Privacy Preserving Data Collection
Lecture Notes in Computer Science, 2011
We study the distributed k-anonymous data collection problem: a data collector (e.g., a medical research institute) wishes to collect data (e.g., medical records) from a group of respondents (e.g., patients). Each respondent owns a multiattributed record which contains both non-sensitive (e.g., quasiidentifiers) and sensitive information (e.g., a particular disease), and submits it to the data collector. Assuming T is the table formed by all the respondent data records, we say that the data collection process is k-anonymous if it allows the data collector to obtain a k-anonymized version of T without revealing the original records to any adversary. In contrast to most k-anonymization approaches which trust the data collector, our work assumes that the adversary can be any third party, including the data collector and the other responders.
Privacy Preserving for Mobile Health Data
collected by most of the enterprises which can be used by researchers for various purposes to perform analysis on the data. Individuals would not like to release their private information directly through any means. Even though the identifying attributes are suppressed, they can be still identified by linking it to the openly available data sources. The K-Anonymity is a protection model that forms the basis for many real-world privacy protection systems. A release provides K-Anonymity protection if the information for each individual contained in the release cannot be distinguished from at least K-1 individuals. This project is an attempt to develop a Structure for privacy protection using K-Anonymity under different scenarios by considering real world medical electronic record sets and to ensure that the principles provide maximum privacy and utility of the data. The project also re-identifies attacks that can be realized on releases that hold to K-Anonymity unless accompanying policies are respected.
Privacy-Preserving Algorithm for Medical Data
International journal of innovations in engineering and science, 2022
Various mobile applications are emerging as a result of the rapid development of mobile internet and the growing popularity of smart terminals. Medical data has evolved into a valuable asset that is constantly assessed and applied, resulting in a significant improvement in the quality of medical care. However, publishing and using user data exposes the user to the possibility of an attack. Medical data carries not only the patient's medical state and medical knowledge, but also the individual's sensitive personal information of a huge number of patients, due to the unique character of the medical profession. Allowing users to fully benefit from social networks while maintaining security is a critical issue that must be addressed immediately in the age of big data. We begin by providing an overview of the privacy hazards of social network data and several sorts of assaults in this study. We propose a privacy protection algorithm based on privacy privacy to leak confidentiality to sensitive social networks. The system employs edge-based weight conversion, which drastically reduces the calculation value and allows for a quicker response from the user. Reduces user leakage of confidential user data while maintaining personal standards under data availability. This strategy, in comparison to more complex ways, protects users against thinking attacks and eliminates the distortion of standard findings produced by data misunderstanding, ensuring the correctness of the suggestions. Our system can ensure effective and long-term security of usersensitive data, according to real-world data sets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09, 2009
Sharing healthcare data has become a vital requirement in healthcare system management; however, inappropriate sharing and usage of healthcare data could threaten patients' privacy. In this paper, we study the privacy concerns of the blood transfusion information-sharing system between the Hong Kong Red Cross Blood Transfusion Service (BTS) and public hospitals, and identify the major challenges that make traditional data anonymization methods not applicable. Furthermore, we propose a new privacy model called LKC-privacy, together with an anonymization algorithm, to meet the privacy and information requirements in this BTS case. Experiments on the real-life data demonstrate that our anonymization algorithm can effectively retain the essential information in anonymous data for data analysis and is scalable for anonymizing large datasets.
A Mixed Model for Privacy Preserving and Secure Sharing of Medical Datasets
Communications in Computer and Information Science
Data mining has been a huge success from the beginning because of what it can offer and do. But there are some concern about the privacy of people in the used data. The problem here is being able to share datasets without putting privacy of the concerned people at risk and without being accessed by unauthorized people. That's where the different anonymization techniques like suppression and generalization can be employed to safeguard privacy of people in the datasets. Privacy preserving data mining (PPDM) has been researched for some years now, but there still many things to do. Privacy concerns are everywhere, but here the author only considered privacy issues with medical dataset. How to protect privacy of people in those dataset while keeping the data in them useful. In this paper, the author present a work which combines anonymization in order to tackle privacy issues in a medical dataset and secure sharing in order to control the access to the datasets. In the end, it is clear that the combination of anonymization and secure sharing offers not only protection against re-identification but also against unauthorized access to a given dataset.