Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions

Framework to Avoid Similarity Attack in Big Streaming Dat

The existing methods for privacy preservation are available in variety of fields like social media, stock market, sentiment analysis, electronic health applications. The electronic health dynamic stream data is available in large quantity. Such large volume stream data is processed using delay free anonymization framework. Scalable privacy preserving techniques are required to satisfy the needs of processing large dynamic stream data. In this paper privacy preserving technique which can avoid similarity attack in big streaming data is proposed in distributed environment. It can process the data in parallel to reduce the anonymization delay.  In this paper the replacement technique is used for avoiding similarity attack. Late validation technique is used to reduce information loss. The application of this method is in medical diagnosis, e-health applications, health data processing at third party

Ensuring patients privacy in a cryptographic-based-electronic health records using bio-cryptography

Several recent works have proposed and implemented cryptography as a means to preserve privacy and security of patients health data. Nevertheless, the weakest point of electronic health record (EHR) systems that relied on these cryptographic schemes is key management. Thus, this paper presents the development of privacy and security system for cryptography-based-EHR by taking advantage of the uniqueness of fingerprint and iris characteristic features to secure cryptographic keys in a bio-cryptography framework. The results of the system evaluation showed significant improvements in terms of time efficiency of this approach to cryptographic-based-EHR. Both the fuzzy vault and fuzzy commitment demonstrated false acceptance rate (FAR) of 0%, which reduces the likelihood of imposters gaining successful access to the keys protecting patients protected health information. This result also justifies the feasibility of implementing fuzzy key binding scheme in real applications, especially fuzzy vault which demonstrated a better performance during key reconstruction

Designing the Health-related Internet of Things: Ethical Principles and Guidelines

The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols

Preserving The Safety And Confidentiality Of Data Mining Information In Health Care: A literature review

Daily, massive volume of data are produced due to the internet of things' rapid development, which has now permeated the healthcare industry. Recent advances in data mining have spawned a new field of a study dubbed privacy-preserving data mining (PPDM). PPDM technique or approach enables the extraction of actionable insight from enormous volume of data while safeguarding the privacy of individual information and benefiting the entire society Medical research has taken a new course as a result of data mining with healthcare data to detect diseases earlier and improve patient care. Data integration necessitates the sharing of sensitive patient information. However, substantial privacy issues are raised in connection with the storage and transmission of potentially sensitive information. Disclosing sensitive information infringes on patients' privacy. This paper aims to conduct a review of related work on privacy-preserving mechanisms, data protection regulations, and mitigating tactics. The review concluded that no single strategy outperforms all others. Hence, future research should focus on adequate techniques for privacy solutions in the age of massive medical data and the standardization of evaluation standards

Internet Predictions

More than a dozen leading experts give their opinions on where the Internet is headed and where it will be in the next decade in terms of technology, policy, and applications. They cover topics ranging from the Internet of Things to climate change to the digital storage of the future. A summary of the articles is available in the Web extras section