As a society, we have seen remarkable advances in the way we communicate and interact over the last 20 years.  Information and communications technology (ICT) continue to evolve not just in the virtual world, but also in the physical world. In this new landscape, we are witnessing physical objects merging into the digital information infrastructure. For example, physical objects such as door locks and medical devices are linked by IP-based networks and transmit information about themselves and their observations. The evolution of communications to include physical objects has implications across a wide range of applications and segments of society, including new challenges for security and privacy.

Known informally as the Internet of Things (IOT) by many and less commonly as proactive technological infrastructure by some, this evolving landscape includes smart devices that gather data and applications that perform statistical data mining on data to anticipate human behavior. This instantiation of the IOT introduces the following ethical quandaries:

  • Is it possible to avoid the IOT? As we move around our daily lives, should we assume that our lives and/or behavior are being monitored at all times?
  • Does the IOT constitute the ultimate utilitarian approach to society? Are we monitoring the lives, or specific aspects of lives, of everyone to reduce risk to each of us?

We knowingly, and sometimes unknowingly, interact with the IOT on a daily basis in both our professional and personal lives. Many organizations are implementing IOT devices to help with decision-making, resource allocation, and operations. As organizations use technology to move to an “always on” environment, users become part of the information infrastructure through the use of their personal mobile devices for applications such as corporate email (thus extending corporate policies onto personal devices). In essence, users become nodes on the IOT.

Today, internet advertisers are able to combine data from various seemingly insignificant activities to create potentially significant profiles. This correlated data allows advertisers to send users targeted advertising as they search the internet for that “must-have” new gadget or the latest song. In fact, targeted web-based ads based on correlated user profiles derived from statistical models are just the first generation of anticipatory services. Data mining will only become more accurate over time at determining our desires and needs.

Many people today wear sensors when they work out or move through their daily lives to track their heart rate, miles traveled, or steps taken. These activity monitor sensors are connected wirelessly to smart phones and to the internet to enable users to track metrics over time. Fitbit, Inc. states the following about the Fitbit One product:

During the day, it tracks your steps, distance, calories burned, and stairs climbed. Come nightfall, it measures your sleep quality, helps you learn how to sleep better, and wakes you in the morning. The One™ motivates you to reach your goals by bringing greater fitness into your life – seamlessly, socially, 24 hours a day.

Consider the following scenario: Depending on the privacy policy in place, a user's fitness information is sold to marketers who have an agreement with her mobile phone provider to share data.  She might be shopping in the supermarket and start receiving coupons via text messages or email (since her phone's geo-location services are enabled) offering 10 percent off the latest weight-loss shake because “it appears you are trying to lose weight and we can help.”  In effect, the IOT is anticipating what she might want to buy based on metrics and her behavior.  She may welcome this opportunity to save money on something she hadn't considered purchasing, for others though, such benign invasiveness is a concern.