A data processing agreement (DPA) is a legally binding document to be entered into between the controller and the processor in writing or in electronic form. It regulates the particularities of data processing- such as its scope and purpose- as well as the relationship between the controller and the processor.
The following document is being used- in conjunction with the Data breach event response framework- to report the specific details of the data breach, as well as the remedial measures that are taken to minimize the risk to data subjects.
Machine Learning algorithms are born as a solution to a problem. Once we feel comfortable with a prototype, there is still a long path to its deployment...and it is rarely a smooth one. We would like to share our experience transiting that path.
Machine Learning algorithms are born as a solution to a problem. Once we feel comfortable with a prototype, there is still a long path to its deployment...and it is rarely a smooth one. We would like to share our experience transiting that path.
Reduced cost by 30%, increased impact and scope by 200% while working in a market undergoing huge foundational change.
Reduced cost by 30%, increased impact and scope by 200% while working in a market undergoing huge foundational change.
Market research is embracing insightful new sources of data. Among them, behavioural data is one of the most promising. It has proved to have an edge over survey data by overcoming human memory limitations and lack of sincerity. The challenge, however, in sharing clickstream data with third parties is to avoid violating individual's privacy rights, as defined in the GDPR. To overcome this difficulty, we developed our first "PII Filter", based on an intuitive principle: public web sites can be accessed by anyone; therefore, those URLs should be visited by several people. As a result, a new PII Filter has been developed based on a much more Aristotelic principle, learning from experience. This new PII Filter relies on a supervised predictive classifier: a rule-based algorithm that learns from a labelled data set of URLs.
Online behavioral data is a valuable source of insights for researchers. However, data collected passively via tracking meters contains Personal Identifiable Information (PII). With the GDPR into force, the value of online behavioral data is constrained by the risk of disclosing PII. We present a machine-learning solution that significantly reduces the risk of revealing PII when sharing browsing data.