Think All SaaS is Created Equal? Think Again.

Privacy concerns are at an all-time high. With the proliferation of devices and the increasing complexity of network environments, the opportunity to use Wi-Fi device data to derive occupancy estimates is compelling, but must be balanced against the demands for security and privacy. While some solutions in this arena have cut corners at the expense of privacy, there are more sophisticated approaches that deliver strong results, within a framework that demands complete privacy. To that end, let’s explore how artificial intelligence (AI) can be leveraged to enhance privacy without the need for linking users to their personal network traffic.

Deduplicating Multiple Devices from Occupancy Analytics

Estimating occupancy based on Wi-Fi requires that you solve a fundamental problem: how do you deal with multiple devices carried by the same person? People are walking around not only with laptops, but other devices like smartwatches, tablets, and laptops, all pinging the Wi-Fi sending signals as if three people are standing in the room.

So exactly how do Wi-Fi occupancy platforms like Lambent Spaces handle multiple devices? Not all platforms are created equal and this is where a history deeply steeped in data privacy and security separates Wi-Fi analytics from one another.

The Basic Approach: Device Identification Logs

The simplest way to use Wi-Fi to collect occupancy analytics is to ask your IT team to export the lists of devices and what accounts they’ve authenticated with and provide that to third parties. This list can include device identifiers like IP addresses, MAC Addresses, and hostnames that can track devices across your network.  In one such example, one provider advertised itself as a “DEI” product, literally reporting on the movements of groups of people who were identified by various classifications.

The Risk of Collecting and Storing PII

While certainly effective, these methods raise significant privacy concerns as they involve collecting detailed information about every device and user on the network. Companies using this method are collecting and keeping information on devices and tracking students and faculty. Lambent Spaces does not use this data collection methodology as we are hypersensitive to our customer’s privacy protection needs.

Higher Education Institutions are particularly sensitive about protecting the privacy of faculty, staff, and students considering the types of information about their constituents they house in records. Faculty and students in particular have pushed back against administrative monitoring through sensors and other invasive technologies, and University leaders have been forced to offer public apologies for overstepping the privacy line in the pursuit of building occupancy metrics.

While Higher Education Institutions are required to measure student success and satisfaction and many strive to continuously improve the quality of student experience as it pertains to academic outcomes, using occupancy analytics as a proxy for engagement on-campus is a bridge too far.

Many institutions see the invasive collection of PII data and the gap between occupancy and engagement to be misaligned with the institution’s objectives and duty to protect sensitive student and faculty data. Occupancy can support real estate decisions by helping drive investment strategies and can correlate with student satisfaction but can’t be a measure of engagement or academic success.

Advanced AI: Devices Without Personal Identification

Lambent Spaces is a privacy-first platform, built with intention to protect the privacy of building users. It’s why we deduplicate devices the hard way with an advanced AI that uses human-like device behavior to remove static devices from occupancy counts. We collect zero PII data related to the devices we analyze.  What limited data we do collect is hashed at the customer network’s edge, meaning reversing the data we have requires keys that only the Institution holds and even if compromised, would simply render a MAC address, with no additional information about its user. In effect, all data is rendered unidentifiable before it comes into the Lambent Platform.

What to Look for to Protect PII in Occupancy Analytics

Lambent ensures that information security best practices, systems and controls are in place and, as a SOC 2 certified company, that policy adherence is verified through formal audits. These protect against unauthorized access, disclosure, or damage that might compromise customer data or system operation. Customer data is logically separated and encrypted in transit and at rest.

If protecting student, faculty, and employee privacy is a paramount concern, look for an occupancy analytics platform that never ingests device identification logs, hashes data at the edge, and is SOC 2 certified.

For more information about how to measure occupancy without invading privacy, talk to a Lambent Spaces sales representative today.