In abundance, digital data divulges sensitive information
The reality is that people generate and share more information than ever before and when it comes to online privacy, each one of us appreciate the risk of publicizing sweetest information such as incriminating videos and photos. But few of people realize a subtler threat that even those who knows have chosen to ignore it. Everyday data can divulge sensitive information as well. Some questions cannot be asked otherwise employers, for instance, generally are not allowed to discriminate based on religious or marital status, sexual orientation and so forth. Our growing digital footprint is threatening our ability to dodge inappropriate inquiries. Through data mining, employers, insurers, advertisers and others can infer the answers to private questions without even asking. Early this year, my insurer sent me a message on Linkedin to remind me of payment due date as they couldn’t get me on phone. Heap of personal data, and the techniques to crunch it are readily available for any organization. Outside consciously generated web content such as blogs, Instagram profiles and YouTube videos, a steady stream of data is exchanged in the background that an ordinary user has no idea about. Mobile companies in Africa are now being accused of tracking their customer online searches and browsing behavior. Computing devices silently disclose users location while he or she post status updates and photos to the Web irrespective of location service being off or not.
Data mining relies on the principle that certain information can take on new meaning when combined with other data. Scientists all over the world already use this technique. The most common is where data integration involves combining different types of data to learn something new. Consider a photograph of a pet. Alone, it’s an abstract representation. However, tagging the photo with home location and a time stamp as well as public listing identifying the pet as lost and that suddenly makes it very meaningful. Second one is the data aggregation. Amassing enough amounts of certain types of data, and a pattern of trends emerges. For instance, an iPhone’s location can be determined by tracking its signal. By aggregating enough location data from a phone, it’s easier to derive an increasingly reliable map of user’s regular routes of travel. With such information, it easier to estimate where the user is likely to be at a given time and perhaps even guess user home location, income and so on. The more personal data trails grow, the more severe privacy threat becomes. So, if you care about protecting individual privacy in a meaningful way, it is also important to prevent third parties from harvesting and crunching data in a manner that circumvents need to ask them at all.