Our Body’s into Sensors and Data?

Richard Lam

Imagine being watched over 24/7 or being monitored by cameras everywhere we go. We can reference fictional books like 1984 or develop crazy imaginative theories for it, but they usually only touch the surface of it. In the healthcare sector, we are at the stage beyond this point of imagination and now there is the capacity to monitor the insides of us from the touch of our mobile devices. Soft Privacy Technology can be applied to the healthcare sector as there is an advancement for smart technology to monitor you at all times. Before giving judgment, let’s look at what we have on our table with this smart technology and how it can help change our lives. I personally use smart technology like Alexa from Amazon which allows me to control the light switch, alarm clock, and many more through voice commands. Another example that you may have as well are those disk-shaped vacuum cleaners known as Roomba that run around the ground, banging to every side to ensure cleanliness. In the healthcare space, they can be used to create technology like Ambient Assisted Living, which monitors and reports sensitive information remotely into a cloud. With the wellbeing of the users at the forefront, these devices detect various biometrics in a patient’s body with sensors. These smart devices can detect any irregularities and give a signal instantly to those that can help to prove to be extremely valuable to many patient’s lives. Due to the nature of the device reporting constant data and usage of smart technology 24/7 they are subject to a lot of privacy concerns. This is where soft privacy comes into question on the effectiveness of the third-party cloud service as they present several privacy concerns including risk in unauthorized access, data leakage, sensitive information disclosure, and privacy disclosure helps ensure our privacy safety. Given at our disposal to protect us are these 3rd party programs equipped with cloud computing, VPNs usage, and multi-level access control. If we use these soft privacy technologies that are put in place we have stronger control over the data that we rightfully own. The question is, are these soft privacy technologies in place trustable and safe for you? Soft privacy technologies are both new in many aspects, but they have gone a long way as a whole to be safe. The process of privacy safety didn’t start here, so techniques that are used to ensure privacy are not new despite the technology being new. So rest assured, the process of handling the data messaging is safe. A solution used by many industries even before Ambient Assisted Living was created is called differential privacy and is often used in many data collection services so it has a proven track record to work. The differential privacy system typically encrypts the data, sends it to a trusted service, then opens it up for the hospital institutions. A strategy that is often used to prevent data leakage and attacks is by adding ‘noise’ into the data which changes the value slightly while accessing the real information through security questions. A study by Sensors concluded that differential privacy techniques involving additional noise helped achieve mathematically-precise guaranteed privacy. Despite technological advancement to increase the amount of “eyes” on us in the world, with soft privacy technology we take control of our own privacy and allow the technology to advance our quality of life. 

References:

  1. Salama, Usama, Lina Yao, and Hye-Young Paik. 2018. An Internet of Things Based Multi-Level Privacy-Preserving Access Control for Smart Living. 5(2):23–40. DOI: https://doi.org/10.3390/informatics5020023

  2. Ren, Hao et al. 2016. Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees. Sensors 16(9):1–25. https://pubmed.ncbi.nlm.nih.gov/27626417/ DOI: 10.3390/s16091463

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  4. Jain, Priyank, Manasi Gyanchandani, and Nilay Khare. 2018. “Differential Privacy: Its Technological Prescriptive Using Big Data.” Journal of Big Data 5(1). https://journalofbigdata.springeropen.com/articles/10.1186/s40537-018-0124-9

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