COVID-19 has forever changed our lives, but the world is finding ways to thrive in the face of adversity.
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Self-Sovereign Identity (SSI) is a model for managing digital identities in which an individual has sole ownership and oversight over how, when and with whom their personal data is being shared. Users who subscribe to this model are able to hold and store all of their information directly on their own device and share for verification without the need of a central aggregator of data (i.e Equifax) - which tend to be highly vulnerable to theft and attack.
Each time you interact with an organization to access services or pay for goods, a large amount of personal information is shared with a variety of agents (for example driver's license, Social Insurance Numbers, emails, etc). Not only is this a cumbersome process for you, but the more information you share the greater the risk is for the security of your data.
At a large organization in an established industry, there will always be“organizational inertia”: that is to say, it’s hard to adapt and respond to new threats (or opportunities) when the organization is busy staying focused on its day-to-day operations and delivering on expectations. The adage “if it’s not broken, don’t fix it” often reigns supreme. Change is hard, and staying focused is key – especially when people’s finances, businesses, and homes are at stake. In recent years, however, the banking industry in Canada hit an inflection point. The processing power of cloud computers has finally reached a critical point where it is now cheaper to rent secure space on a server, rather than buy and house data stores on-premise. Not to mention the data loss risk reduction provided by redundancy systems in cloud storage.
In order to build and maintain customer trust, financial institutions are investing millions of dollars in preserving privacy while experimenting with exponential technology to bring best value to their customers. With rapid advancements in artificial intelligence on all fronts, however, there is a dire need in the financial sector to balance customer privacy with the use of transaction data in training robust machine models. This white paper provides a rationale focused on data privacy and also includes suggested algorithmic recipes related to how transaction data could completely eradicate the use of real customer data. A distinction is drawn between data simulation and synthesis. Two discussed synthesis techniques include a bootstrapping framework featuring dynamic time warping and a hidden Markov model fitted with a stochastic gradient version of the Markov chain Monte Carlo method.