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Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy

arXiv.org Artificial Intelligence

Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.


The 2018 Survey: AI and the Future of Humans

#artificialintelligence

"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.


The Future of AI Part 3

#artificialintelligence

This article will focus on the impact of AI, 5G, Edge Computing on the healthcare sector in the 2020s as well as a section on Quantum Computing's potential impact on AI, healthcare and financial services. The next in the series will deal with how we can use AI in the fight against climate change including the protection of the Amazon, smart cities and AGI. For those who are new to AI, Machine Learning and Deep Learning, I recommend taking a look at the following article entitled "An Introduction to AI." I will refer to Machine Learning and Deep Learning as being subsets of AI. Furthermore, this article is non-exhaustive in relation to potential applications of AI to healthcare and Quantum Computing to various sectors of the economy. The reason for the focus on AI in healthcare is in light of recent articles by a few senior medical practitioners in the US expressing concern about the role of AI in healthcare. Some of the concerns expressed such as the need for improved sharing of data ...


The Future of AI Part 3

#artificialintelligence

This article will focus on the impact of AI, 5G, Edge Computing on the healthcare sector in the 2020s as well as a section on Quantum Computing's potential impact on AI, healthcare and financial services. The next in the series will deal with how we can use AI in the fight against climate change including the protection of the Amazon, smart cities and AGI. For those who are new to AI, Machine Learning and Deep Learning, I recommend taking a look at the following article entitled "An Introduction to AI." I will refer to Machine Learning and Deep Learning as being subsets of AI. Furthermore, this article is non-exhaustive in relation to potential applications of AI to healthcare and Quantum Computing to various sectors of the economy. The reason for the focus on AI in healthcare is in light of recent articles by a few senior medical practitioners in the US expressing concern about the role of AI in healthcare. Some of the concerns expressed such as the need for improved sharing of data ...


Tracking your pregnancy on an app may be more public than you think

Washington Post - Technology News

Like millions of women, Diana Diller was a devoted user of the pregnancy-tracking app Ovia, logging in every night to record new details on a screen asking about her bodily functions, sex drive, medications and mood. When she gave birth last spring, she used the app to chart her baby's first online medical data -- including her name, her location and whether there had been any complications -- before leaving the hospital's recovery room. But someone else was regularly checking in, too: her employer, which paid to gain access to the intimate details of its workers' personal lives, from their trying-to-conceive months to early motherhood. Diller's bosses could look up aggregate data on how many workers using Ovia's fertility, pregnancy and parenting apps had faced high-risk pregnancies or gave birth prematurely; the top medical questions they had researched; and how soon the new moms planned to return to work. "Maybe I'm naive, but I thought of it as positive reinforcement: They're trying to help me take care of myself," said Diller, 39, an event planner in Los Angeles for the video game company Activision Blizzard.


ML helps health plans tackle SDOH, improve outcomes

#artificialintelligence

With the passage of the Chronic Care Act, Medicare Advantage plans have been scrambling to figure out how to offer supplemental benefits to their members. Passed as part of a Bipartisan Budget Act last year, the Chronic Care Act promotes the use of benefits that maintain health or keep a beneficiary's health from deteriorating, and the benefits don't have to be health-related. Instead, they can include help for social determinants of health that include housing, nutrition and transportation. Under the act, the supplements can also be tailored to the individual, when it comes to qualifications. The same benefits don't have to be offered to every beneficiary, he says.


Will tech companies change the way we manage our health?

#artificialintelligence

As of September 2018, the top 10 tech companies in the U.S. had spent a total of $4.7 billion on healthcare acquisitions since 2012. The number of healthcare deals undertaken by those companies has consistently risen year-on-year. It all points to an increasing interest from technology companies in U.S. healthcare, which raises many questions as to what their intentions are, and what the ramifications will be for the health industry. It also begs the question as to why healthcare has become the latest target of U.S. tech giants. On the surface, they don't seem like natural bedfellows.


Advances in artificial intelligence threaten privacy of people's health data

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Advances in artificial intelligence have created new threats to the privacy of people's health data, a new University of California, Berkeley, study shows. Led by UC Berkeley engineer Anil Aswani, the study suggests current laws and regulations are nowhere near sufficient to keep an individual's health status private in the face of AI development. The research was published Dec. 21 in the JAMA Network Open journal. The findings show that by using artificial intelligence, it is possible to identify individuals by learning daily patterns in step data, such as that collected by activity trackers, smartwatches and smartphones, and correlating it to demographic data. The mining of two years' worth of data covering more than 15,000 Americans led to the conclusion that the privacy standards associated with 1996's HIPAA (Health Insurance Portability and Accountability Act) legislation need to be revisited and reworked.


Artificial intelligence advances threaten privacy of health data

#artificialintelligence

Advances in artificial intelligence have created new threats to the privacy of people's health data, a new University of California, Berkeley, study shows. Led by UC Berkeley engineer Anil Aswani, the study suggests current laws and regulations are nowhere near sufficient to keep an individual's health status private in the face of AI development. The research was published Dec. 21 in the JAMA Network Open journal. The findings show that by using artificial intelligence, it is possible to identify individuals by learning daily patterns in step data, such as that collected by activity trackers, smartwatches and smartphones, and correlating it to demographic data. The mining of two years' worth of data covering more than 15,000 Americans led to the conclusion that the privacy standards associated with 1996's HIPAA (Health Insurance Portability and Accountability Act) legislation need to be revisited and reworked.


Advances in AI threaten health data privacy: Study

#artificialintelligence

Advances in artificial intelligence (AI) have created new threats to the privacy of health data, a study has found. The study, published in the journal JAMA Network Open, suggests current laws and regulations are nowhere near sufficient to keep an individual's health status private in the face of AI development. The research led by professor Anil Aswani from the University of California -- Berkeley in the US, shows that by using AI, it is possible to identify individuals by learning daily patterns in step data like that collected by activity trackers, smartwatches and smartphones, and correlating it to demographic data. The mining of two years' worth of data covering over 15,000 Americans led to the conclusion that the privacy standards associated with 1996's HIPAA (Health Insurance Portability and Accountability Act) legislation need to be revisited and reworked. "We wanted to use NHANES (the National Health and Nutrition Examination Survey) to look at privacy questions because this data is representative of the diverse population in the US," Aswani said.