Information and Privacy Commissioner of Ontario and Aislelabs discuss design and architecture of MLA systems.
As the popularity of smartphones and tablet computers continues to rise, more and more creative ways of using these devices are being developed. One technology that has recently created a new use of smart mobile devices by utilizing their increased connectivity is Mobile Location Analytics (MLA). At Aislelabs, protecting consumer privacy is of utmost importance to us. As a result, we are releasing a whitepaper detailing the architecture of our system to provide full transparency in our technology and business practices.
This paper is co-authored with the Information and Privacy Commissioner, Ontario, Dr. Ann Cavoukian. Dr. Cavoukian is recognized as one of the leading privacy experts in the world. An avowed believer in the role that technology can play in protecting privacy, Dr. Cavoukian’s leadership has seen her office develop a number of tools and procedures to ensure that privacy is protected in Ontario – and around the world. Dr. Cavoukian is best known for her mantra of “Privacy by Design” and businesses across North America and Europe regularly seek Dr. Cavoukian’s advice and guidance on privacy and data protection issues.
Aislelabs’ co-founder and CEO, Dr. Nick Koudas, said, “We are incredibly honoured to be working with the Information and Privacy Commissioner Ontario on this paper, outlining potential pitfalls of the technology if not implemented properly, and privacy protecting measures. We have designed our products to respect the user’s right to privacy from the very beginning, and this whitepaper further validates or commitment to privacy”.
In this paper, we outline the application of ‘Privacy by Design’ principles in the design and architecture of MLA systems and their corresponding business practices to allow consumer privacy and retail analytics to co-exist. It begins with a background discussion of MLA and how it works technologically. Next the paper discusses the unique privacy risks associated with MLA (section 3). Finally, it introduces Privacy by Design, discusses Aislelabs’ MLA implementation, and shows how it designs in privacy from the outset. The paper also provides examples showing how both retailers and consumers benefit from the results of MLA technology—a positive-sum “win-win” outcome in its own right. However, when combined with the fact that this benefit does not come at the expense of individuals’ privacy, but rather individuals’ privacy may be protected at the same time without diminishing system functionality, this positive-sum outcome becomes a “win-win-win.” Consumers benefit from not only better services and prices but by the fact that their privacy is protected throughout the system by appropriate measures being proactively embedded into its design and architecture.
Aislelabs provides an intelligent cloud platform for real-time in-store marketing. Company’s product suite is enabling new ways for retailers to better understand their consumers and engage with personalized experiences based on consumer needs. As a result, Aislelabs’ advanced technology is setting the stage for the next generation of retail experiences in a safe and secure fashion.
Aislelabs is also a key member of the Washington D.C.-based think tank Future of Privacy Forum, and has proactively identified privacy issues inherent in the functioning of MLA technology. The result is a Code of Conduct that sets down effective privacy controls for the use of MLA technology in the industry. The code as adopted by Aislelabs plays a preventive, and not just remedial, role in upholding the users right to privacy by providing full transparency and choice to the consumer.