Big Data and Privacy by Design
In my previous article, I discussed the concept of data privacy and how it evolved. Let us now discuss the concept of Big Data.
Basics
Big Data is a common term referring to a data revolution in information technology that makes it easy to collect, store and analyze user data online at relatively low costs.
In simpler words, any human activity using technology leaves a ‘digital exhaust’ or a trace data — let’s say, a footprint. For example, using your phone to make a call or using your prepaid card at a metro station leaves behind your location/position coordinates. Broadly speaking, the big pool of all these collected footprints is called Big Data. However, it’s not just a collection of these footprints but it also contains various other information like weather, train information, payments, etc.
Generally these footprints may not have any apparent or obvious meaning, but they start to make sense when combined with other recorded datasets. For example, the number of times a call was made from a specific location to a specific number. This information could be processed using powerful analytic tools to give greater meaning and context to it while also enabling the system to ‘predict’ the unknown or missing information in the dataset.
Today, we are already surrounded by a sea of ubiquitous sensors (sensors on your phones, punching access cards or swiping credit cards, etc). With each advancement, like the advent of the Internet of Things, coupled with the ‘smartphone revolution’ linking more and more information to to your social media accounts, it is getting easier to gather more information and make sense of it.
This sounds useful, what’s the controversy?
In London, April 2014, James Siddle used the open data provided by the Transport of London on Bike Share and used it to paint a map of the movements of individual customers.
He also analyzed the datasets to create movement patterns using various filters and also linked them to different activities of the users. He claims that it required just one further step to accurately predict the identity of the individuals.
In another incident, Target (a popular retail chain in the US) predicted the pregnancy of a girl even before her father could find out and sent her advertisements about baby cribs and clothes.
‘Predictive analytics’ in generating behavior patterns of individuals does sound controversial right? James Siddle case became a widely talked about event, raising concerns about how, without us knowing, our location data could be ‘alarmingly’ used to make sense; predicting identities, movements, connections, political beliefs or even relationships. The users care if someone tracks them and gets the history of all their past activities.
Privacy by Design
Recently, I downloaded the AirtelTV app only because I wanted to claim the ‘free Amazon Prime subscription’ that was being offered and the only way to do it was from inside the app! (Yes you heard it right.)
In order for me to open the app, I had to agree to these three asks by Airtel TV app:
I still don’t get why an app which basically deals with showing on-demand video content, asked me for access to my contacts, photos, files and other media. The more I think about it, the more it gets on my nerves. However, at that point, I only cared about getting the Prime subscription so I went ahead with installing it and have never used the app since then.
The fact of the matter remains that in spite of knowing about privacy concerns regarding our personal data, the perception of ‘what we gain’ is more tangible and hence overpowers the realization of what are we trading off. For example, most people installed Truecaller at the pretext of knowing unknown callers (a tangible outcome), however, its ask for permission to access their contacts was probably overlooked as the outcome of that wasn’t immediately comprehensible (hence not tangible).
Personalization, when coupled with high privacy assurance, creates a significant positive association. It makes users more willing to share personal information and adopt a web based service. This highlights the importance of the user experience in terms of the perception of ‘being in control’ and the assurance for privacy. Which of the below messages are you more willing to respond with an ‘allow’?
As designers, it is important for us to build a level of trust with our users by bringing about a ‘transparency’ through design. There are various ways to do it. Maybe I could cover a few in some later posts but what is important is that we consciously ‘design for privacy’.
REFERENCES
Lee Chung, H.; Cranage David, A. 2010. Personalisation-privacy paradox: The effects of personalisation and privacy assurance on customer responses to travel websites. Elsevier. http://www.elsevier.com/locate/tourman
Yanying Gu, Anthony Lo, 2009. A Survey of Indoor Positioning Systems for Wireless Personal Networks. IEEE Communications Surveys & Tutorials, Vol. 11, №1, First Quarter.
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