Cybersecurity, Health Information & Artificial Intelligence
Protecting Health Information Using Artificial Intelligence
This may be the Time To Fight The Hackers of Patient Information with the Same Tools They Exploit!
For decades human beings have been striving to build a machine that can think like them, process information the way they do, and carry out tasks faster and with the utmost precision. Artificial Intelligence, or AI today, offers precisely that by simulating the human intelligence process by computers and machines.
Artificial Intelligence can disrupt industries, markets, and societies positively. It also comes with many risks in the wrong hands and possession of states and corporations, negatively impacting individual liberty and human rights.
Utilities of Artificial Intelligence Holds its Particular Incongruity
The utility of Artificial Intelligence is merely endless. Depending on the purpose of use and mindset of its architect, AI can be anything from detecting technical defects and predictive maintenance to transforming consumer experience and influencing user mindset through engagement.
Abusive solutions powered by Artificial Intelligence are just as numerous as the number of solutions intended for legitimate utility. For instance, cybercriminals today can use AI tools to mimic human behavior and enable themselves to con a "bot detection system" on social media and other platforms imitating humans.
The immensely diversified application of Artificial Intelligence also implies a diversity of its associated problems and risk.
Artificial intelligence in the wrong hands helps efficiently deanonymize anonymous patient information and privileged medical information. That is by identifying and tracking someone, some process, or metadata across various platforms and devices irrespective of where the latter interaction or transaction transpires.
Artificial intelligence can find, identify, and track patients, physicians, and stakeholders through sensitive facial-equipped recognition cameras. Thus it has the potential to transform anonymity expectations in the public stretches.
Artificial Intelligence drove identification, profiling, and automated decision-making process is a potential grounds for discrimination and unfair treatment of patients and medical professionals.
It should not come as a surprising lack of transparency and secrecy of Artificial Intelligence algorithms, code structure, and their architect's intent makes it challenging for the regulatory agencies to enforce fairness and probe negative consequences.
Patient data and information exploitation are much simpler using Artificial Intelligence. Unfortunately, patients and medical professionals often need help comprehending the large volume and value of data their devices and computers generate, process, and share. In this terrain, the utility of AI amplifies this asymmetry and diverts the profits away from legitimate users to information pirates.
Malicious Uses of Artificial Intelligence in Healthcare
Artificial Intelligence has opened many avenues to existing malicious tools that threaten health information. Some of these hostile tools are still in infancy, yet deemed practical soon. One such technology is AI Malware, AI Cloud services abuse, Abusing Smart Assistants, AI-Supported Password Guessing, AI-Supported CAPTCHA Breaking, AI-Aided Encryption, Human Impersonation, and AI- Supported hacking.
How about Fighting Hackers and Abusers of Health Information Using Artificial Intelligence?
Now that we know how Artificial intelligence can contribute to health information abuse, can we use Artificial Intelligence to fight information pirates?
One essential utility security solution that utilizes AI to identify safe versus malicious behaviors in cyberspace. That is, the behaviors across environments with similar circumstances. Also called the process of "unsupervised learning," where Artificial Intelligence meant to enhance security is the product of "Unsupervised" Machine Learning (ML) protocols and algorithms. The latter contrasts supervised learning, which requires some form of human supervision.
Artificial Intelligence can help prevent future patient information piracy by learning from earlier experiences and malware to forecast future acrimonious encounters. AI solutions can configure the system to respond automatically to the imminent cyber threat.
Artificial Intelligence protects patient information through a process called behavior modeling.
Behavioral modeling is a method for better understanding and predicting hackers operate. It employs available health data and information to estimate future behavior in explicit events.
Identifying malware using Artificial Intelligence automates steps to counter security and patient privacy infringements. That process can take a considerable time for a person to detect. In contrast, AI stands up to no such limitations.
Artificial Intelligence security solutions can pinpoint, foresee, react to, and learn about conceivable cybersecurity threats by contextualizing and concluding new or vague data or behaviors and offer attainable solutions to threats or security susceptibilities.
Artificial Intelligence allows healthcare systems to quickly process vast volumes of data and close the gap associated with cybersecurity teams. It provides more consistent and longer-term protection for institutions.
Even more sophisticated techniques and technologies are currently in the pipeline. Solutions such as privacy-preserving hybrid systems incorporate federated learning.
A hybrid approach that combines federated learning with other systems is an emerging technique to preserve privacy.
The "Federated learning" process leans on the participants, such as hospitals, clinics, and medical facilities. It does so by extracting relevant details from its data source to share with the stakeholders without sharing their proprietary information and patients' confidential data.
The federated learning-powered system utilizes Artificial Intelligence to compute data, selectively extract relevant data parameters, and share with discretion. That prevents sharing patients' sensitive information with 3rd party without their consent.
Let us emphasize that Federated learning can serve as an instrument of corporate monopoly and misuse of patient information, thus deserves exclusive attention and transparency on its intended usage.
References
- Artificial Intelligence | Privacy International. "Artificial Intelligence | Privacy International." Accessed November 8, 2022. https://privacyinternational.org/learn/artificial-intelligence
- Kerry, Cameron F. "Protecting Privacy in an AI-Driven World." Brookings, February 10, 2020. https://www.brookings.edu/research/protecting-privacy-in-an-ai-driven-world/
- News-Medical.net. "How Can We Use AI to Preserve Privacy in Biomedicine?" September 29, 2022. https://www.news-medical.net/life-sciences/Using-AI-to-Preserve-Privacy-in-Biomedicine.aspx
- TABRIZ, Dr. ADAM. "Federated Learning Is a Deep Learning Technology with Poker Chip Mission Potential." Medium, September 24, 2021. https://medium.com/technology-hits/federated-learning-is-a-deep-learning-technology-with-poker-chip-mission-potential-ae49e9400edc
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