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In recent years, the Internet of Things (IoT) has gained increasing popularity due to the usage of Internet-enabled devices. However, Internet-enabled devices, also known as smart devices, share the information using an insecure channel, i.e., the Internet. Hence, the security and privacy of shared information remain the biggest concern. To ensure both security and privacy, many smart card based and biometric based schemes have been proposed for different Internet-based applications. Telecare Medical Information System (TMIS) is such an application which makes medical treatment easier by interacting with the patient and doctors. However, the transmission of the patient’s private information over an insecure channel is prone to several attacks. In order to protect the medical privacy of the patient and the reliability of the system, both the patient and medical server should be mutually authenticated. In this paper, we propose a three factor-based authentication scheme for health care system using IoT enabled devices (TFASH) that are secure and more efficient than other relevant schemes. We use Elliptic Curve Cryptography (ECC) for the scheme due to its smaller key size and high level of security. The session key security and the mutual authentication of the TFASH scheme have been proved using Real-Or-Random (ROR) model and Burrows–Abadi–Needham (BAN) logic. The simulation result of the proposed scheme shows that the scheme is safe under the OFMC and CLAtSe models. Moreover, compared to the existing schemes, the TFASH scheme provides better communicational and computational cost, which makes it suitable for practical use.

Background Various methods based on k-anonymity have been proposed for publishing medical data while preserving privacy. However, the k-anonymity property assumes that adversaries possess fixed background knowledge. Although differential privacy overcomes this limitation, it is specialized for aggregated results. Thus, it is difficult to obtain high-quality microdata. To address this issue, we propose a differentially private medical microdata release method featuring high utility. Methods We propose a method of anonymizing medical data under differential privacy. To improve data utility, especially by preserving informative attribute values, the proposed method adopts three data perturbation approaches: (1) generalization, (2) suppression, and (3) insertion. The proposed method produces an anonymized dataset that is nearly optimal with regard to utility, while preserving privacy. Results The proposed method achieves lower information loss than existing methods. Based on a real-world case study, we prove that the results of data analyses using the original dataset and those obtained using a dataset anonymized via the proposed method are considerably similar. Conclusions We propose a novel differentially private anonymization method that preserves informative values for the release of medical data. Through experiments, we show that the utility of medical data that has been anonymized via the proposed method is significantly better than that of existing methods.

Hungary’s special approach to political and social questions is frequently analysed by the media. Taking a closer look at the details of the privacy regulations, we can also identify several unique features. For Hungary, the era of socialism ended with the proclamation of the new Republic of Hungary on 23rd October, 1989. The substantially renewed constitution was entered into force on the same day. The fundamental rights defined in the European Convention of Human Rights were included in it and, in addition, the right to the protection of personal data. The main characteristic of the legal system is that politicians understand the constitution so that it would always require passing a new law whenever they want a public authority to collect personal data for some purpose. By now, Hungary has several hundred laws and decrees in effect on the obligatory collection and transfer of personal data. The nature of such types of regulation ab ovo excludes the possibility of objection and challenging a regulation before the court. Turning our attention to healthcare, we see that medical authorities and researchers are authorized by the law to collect personal medical data without providing information and the right to object to data subjects. The author has been studying the changes in the medical privacy regulations since 2004. Here, he gives an account of the key characteristics of the Hungarian legislation and provides a comparison with the relevant European Union legislation in relation to medical data. When the EU General Data Protection Regulation came into force, the conflict between the two became obvious. This paper gives the reader an expert’s view of the Hungarian data protection policy, while attempting to pave the way to stimulate social debate concerning the necessity for better, and more privacy aware legislation.

This article profiles computer scientist Latanya Sweeney who runs the Data Privacy Laboratory at Carnegie Mellon University. Sweeney's job is to develop software that protects computer privacy and she urges engineers to design new technology with privacy in mind. Sweeney and her students work on issues of identity theft, medical privacy and camera surveillance. She thinks of her team as a digital detective agency.

washington A republican senator has introduced a bill that does not impose heavy restrictions on researchers and industry seeking access to identifiable patient information.

washington A Congressional effort to write a comprehensive law protecting medical privacy was launched publicly last week.