Application of Data Masking in Achieving Information Privacy

Application of data masking in achieving information privacy is implemented in order to enhance privacy of sensitive data. Data privacy is something that is appreciated by all people in different works of life, not only for self-pride but also for security. Think of a medical institution and his patient: the medical client will appreciate the secrecy of his medical data by the medical personnel; in the case of a bank and his client: the client will appreciate the privacy of his account information; just to mention but few cases. The challenge in data privacy is to share data while protecting personally identifiable information. This research work gives detailed analytical information on application of data masking in achieving information privacy. It covers issues on how data in production environment are being masked to avoid exposure of sensitive of sensitive data to an unauthorized user. The information used for this study was extracted from relevant textbooks, journals and internet. A customer-based application was developed to illustrate this subject. Java (using netbeans as the IDE) and MySQL were used as the programming language and back-end database respectively in the development of the said application

Medical image encryption techniques: a technical survey and potential challenges

Among the most sensitive and important data in telemedicine systems are medical images. It is necessary to use a robust encryption method that is resistant to cryptographic assaults while transferring medical images over the internet. Confidentiality is the most crucial of the three security goals for protecting information systems, along with availability, integrity, and compliance. Encryption and watermarking of medical images address problems with confidentiality and integrity in telemedicine applications. The need to prioritize security issues in telemedicine applications makes the choice of a trustworthy and efficient strategy or framework all the more crucial. The paper examines various security issues and cutting-edge methods to secure medical images for use with telemedicine systems

Protecting Privacy and Enabling Pharmaceutical Sales on the Internet: A Comparative Analysis of the United States and Canada

The Internet raises enhanced and unique concerns regarding informational health privacy and Internet pharmacy sales. As technology advances and the Internet changes the way people obtain medical services and products, protecting consumers and their informational health data in online pharmaceutical transactions is paramount. This Comment charts and compares the existing legal frameworks in the United States and Canada relative to informational health privacy. Following this discussion, each legal framework comes into sharp focus with regard to Internet pharmacy sales. Ultimately, this Comment concludes that based on the highly sensitive nature of personal medical information, a baseline privacy standard should be adopted at the federal level to provide consumers with meaningful protection and redress. To realize the benefits of online pharmaceutical transactions, there should be national standards for licensure, as well as continued tough enforcement of laws targeting rogue Web site operators, enabling this valuable medium to flourish

Unlocking Accuracy and Fairness in Differentially Private Image Classification

Privacy-preserving machine learning aims to train models on private data without leaking sensitive information. Differential privacy (DP) is considered the gold standard framework for privacy-preserving training, as it provides formal privacy guarantees. However, compared to their non-private counterparts, models trained with DP often have significantly reduced accuracy. Private classifiers are also believed to exhibit larger performance disparities across subpopulations, raising fairness concerns. The poor performance of classifiers trained with DP has prevented the widespread adoption of privacy preserving machine learning in industry. Here we show that pre-trained foundation models fine-tuned with DP can achieve similar accuracy to non-private classifiers, even in the presence of significant distribution shifts between pre-training data and downstream tasks. We achieve private accuracies within a few percent of the non-private state of the art across four datasets, including two medical imaging benchmarks. Furthermore, our private medical classifiers do not exhibit larger performance disparities across demographic groups than non-private models. This milestone to make DP training a practical and reliable technology has the potential to widely enable machine learning practitioners to train safely on sensitive datasets while protecting individuals' privacy

Protecting Patient Privacy: Strategies for Regulating Electronic Health Records Exchange

The report offers policymakers 10 recommendations to protect patient privacy as New York state develops a centralized system for sharing electronic medical records. Those recommendations include:Require that the electronic systems employed by HIEs have the capability to sort and segregate medical information in order to comply with guaranteed privacy protections of New York and federal law. Presently, they do not.Offer patients the right to opt-out of the system altogether. Currently, people's records can be uploaded to the system without their consent.Require that patient consent forms offer clear information-sharing options. The forms should give patients three options: to opt-in and allow providers access to their electronic medical records, to opt-out except in the event of a medical emergency, or to opt-out altogether.Prohibit and sanction the misuse of medical information. New York must protect patients from potential bad actors--that small minority of providers who may abuse information out of fear, prejudice or malice.Prohibit the health information-sharing networks from selling data. The State Legislature should pass legislation prohibiting the networks from selling patients' private health information

Processing to Protect Privacy and Promote Access: A Study of Archival Processing in Medical Archives, Health Sciences Collections, and History of Medicine Collections

This research reports on the findings of a study of archival processing in medical center archives, health sciences collections, and history of medicine collections. This exploratory study examined how archivists in these settings process collections and, in so doing, how they balance the potentially conflicting needs of protecting privacy and providing timely access. Four practicing archivists were interviewed, the interviews were transcribed, and data were coded inductively. Participants addressed how they identified sensitive information scattered throughout collections, the impact this sensitive information had on processing decisions, how they communicated access restrictions, and ways in which they managed access. The findings suggest that sensitive information is best protected when it becomes a shared commitment and a shared responsibility between all groups involved.Master of Science in Library Scienc

Medical Privacy and Big Data: A Further Reason in Favour of Public Universal Healthcare Coverage

Most people are completely oblivious to the danger that their medical data undergoes as soon as it goes out into the burgeoning world of big data. Medical data is financially valuable, and your sensitive data may be shared or sold by doctors, hospitals, clinical laboratories, and pharmacies—without your knowledge or consent. Medical data can also be found in your browsing history, the smartphone applications you use, data from wearables, your shopping list, and more. At best, data about your health might end up in the hands of researchers on whose good will we depend to avoid abuses of power.2 Most likely, it will end up with data brokers who might sell it to a future employer, or an insurance company, or the government. At worst, your medical data may end up in the hands of criminals eager to commit extortion or identity theft. In addition to data harms related to exposure and discrimination, the collection of sensitive data by powerful corporations risks the creation of data monopolies that can dominate and condition access to health care. This chapter aims to explore the challenge that big data brings to medical privacy. Section I offers a brief overview of the role of privacy in medical settings. I define privacy as having one’s personal information and one’s personal sensorial space (what I call autotopos) unaccessed. Section II discusses how the challenge of big data differs from other risks to medical privacy. Section III is about what can be done to minimise those risks. I argue that the most effective way of protecting people from suffering unfair medical consequences is by having a public universal healthcare system in which coverage is not influenced by personal data (e.g., genetic predisposition, exercise habits, eating habits, etc.)

Differentially Private Release of Heterogeneous Network for Managing Healthcare Data

With the increasing adoption of digital health platforms through mobile apps and online services, people have greater flexibility connecting with medical practitioners, pharmacists, and laboratories and accessing resources to manage their own health-related concerns. Many healthcare institutions are connecting with each other to facilitate the exchange of healthcare data, with the goal of effective healthcare data management. The contents generated over these platforms are often shared with third parties for a variety of purposes. However, sharing healthcare data comes with the potential risk of exposing patients’ sensitive information to privacy threats. In this article we address the challenge of sharing healthcare data while protecting patients’ privacy. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet , an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets