Augmented Mental Health as part of the Internet of Things (IoT) is kindly bringing about the death of psychotherapy as we know it in providing personalized, real-time data-informed therapy like never before. When FortiGuard Labs reports that healthcare is now experiencing twice the number of cyber attacks than other industries and IoT devices are becoming a bigger target, how are data privacy and security measures evolving to protect some of THE richest and most personal data out there?
Augmented Mental Health: The Psychological Arm of the Internet of Medical Things
There is no stopping the IoT healthcare revolution, also known as the Internet of Medical Things (IoMT). Today, there are 3.7 million medical devices in use that are connected to and monitor various signals from the body to inform healthcare decisions, where a $60 billion global IoT healthcare market in 2014 is forecasted to reach $136.8 billion worldwide by 2021 (according to a report by Allied Market Research).
Augmented Mental Health is essentially an umbrella term to describe the IoMT-based mental health care models, systems, and practices set to dominate the industry—it is the mental health arm of the IoMT.
Augmented Mental Health systems leverage objective emotion and mental health data that can be obtained from a network of intelligently connected smart devices including smartphones and wearables. This allows mental health professionals, as well as integrative care and support teams, to use the patient’s real-time mental health status and trended mental health data to affordably provide highly personalized therapy and remote access to round-the-clock, intelligently stepped care.
Moving from next to no objective mental health data to big data, quite literally overnight, is prompting the intelligent research and design of more rigorous data privacy and security measures.
Augmented Mental Health Data Security Challenges
While data sharing rules play an important role in preserving privacy, the explosive growth of the IoMT has set alarm bells ringing of potential data security nightmares if data protection practices do not keep up with pace.
In response, device manufacturers like Texas Instrument Inc.have built-in state-of-the-art hardware security technology incorporated into their devices. However, this does not guarantee data protection in the IoT environment when data leaves the device and enters health information systems in the cloud.
The fear is that sensitive data is at risk of network sniffing and other malicious hacking, where personal vitals from wearables are particularly vulnerable. Sniffing is a particularly dangerous “passive” type of attack, where the hackers can be silent/invisible on the network, making it difficult to detect.
For Augmented Mental Health in the IoMT, relying on wearables and real-time analysis of and response to patient data means that the devices are constantly running and ready to send data anytime a requesting node makes a demand. Even if not in broadcasting mode, data from digital health devices that stream to other devices, like smartphones, can be compromised.
As put by the chief consumer security evangelist at Intel Security, Gary Davis:
"The information that's contained on your wearable that's stored either on your smartphone or stored downstream on a cloud [service] is worth ten times that of a credit card on a black market."
The more personalized the stolen data is, the more personalized scams can be, increasing the chances of deception. Hacking augmented mental health data, for example, could be used to profit from fraudulent mental health claims. Another scenario is where personalized messages appearing to come from a therapist or fellow patient regarding last nights exposure therapy, could prompt the target to explore malicious links that put the user’s mobile device in danger.
Amidst a global mental health crisis where wearable data is set to be the saving grace, ensuring that strong data privacy measures are in place for the IoMT is now a top prerogative in healthcare to ensure this highly personalized data is safe from malicious third parties and cybercriminals.
Preparing Data Privacy & Security Practices and Policies for the Era of Augmented Mental Health
In order to protect users’ privacy and security, researchers and developers are continually testing and improving privacy-oriented IoT architectures, ensuring Augmented Mental Health data will only be delivered to the nodes that subscribe to receive the information, including those of third parties—as approved by the patient.
When it comes to data privacy, Augmented Mental Health systems are being designed that put the user in control of the quantified self. It’s all about self-monitoring. And the user wanting to use the system in the first place. The user decides who is seeing what data and when. Typically, the select, entrusted circle includes a dedicated therapist and/or care team, as well as key family or friends.
Just as a patient has control over the sharing of sensitive personal information verbally with a therapist, who is bound by Hippocratic oath, augmented mental health systems extend this relationship into the digital space by design.
When it comes to data security, healthcare is getting ahead of the curve with new data encoding-encryption techniques and algorithms continually being improved, such as using meta-data level encryption where the identifiable components of the communicating devices (e.g., serial number or MAC address) are also encrypted.
But what if the key is also compromised? K-anonymity and similar methods are becoming increasingly sophisticated so that data can be shared without revealing who the data belongs to. Anonymous Alice is not so anonymous when even basic health data is connected to behavior data like the type of activity and user location. This is why sophisticated k-anonymity models are particularly important with rich augmented mental health data.
Data provenance tracking is another technique that is already used in the Electronic Health System for management of medical data and is currently being developed for wearables in IoMT applications. Data provenance can be used to generate a historical record of the data and where it’s been, which can be shared with the user to ensure transparency with a full digital audit trail with end-to-end visibility.
Thankfully, these very recent advances in IoMT data privacy and multi-layered security approaches are continually evolving to keep up with the rapid evolution and spread of augmented health and the increasingly sophisticated cyber attacks that come with it.
de Montjoye, Y., Hidalgo, C., Verleysen, M., & Blondel, V. (2013). Unique in the Crowd: The privacy bounds of human mobility. Scientific Reports, 3(1). doi: 10.1038/srep01376
Hiremath, S., Yang, G., & Mankodiya, K. (2014). Wearable Internet of Things: Concept, Architectural Components and Promises for Person-Centered Healthcare. Proceedings Of The 4Th International Conference On Wireless Mobile Communication And Healthcare - "Transforming Healthcare Through Innovations In Mobile And Wireless Technologies". doi: 10.4108/icst.mobihealth.2014.257440
Lambiotte, R., & Kosinski, M. (2014). Tracking the Digital Footprints of Personality. Proceedings Of The IEEE, 102(12), 1934-1939. doi: 10.1109/jproc.2014.2359054
Liu, F., & Li, T. (2018). A Clustering K -Anonymity Privacy-Preserving Method for Wearable IoT Devices. Security And Communication Networks, 5, 1-8. doi: 10.1155/2018/4945152
Lomotey, R., Sofranko, K., & Orji, R. (2018). Enhancing Privacy in Wearable IoT through a Provenance Architecture. Multimodal Technologies And Interaction, 2(2), 18. doi: 10.3390/mti2020018
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