Privacy is a fundamental human right. Protecting personal information should be a core value for organizations, businesses or governments. But how exactly can privacy be defined and how can it be guaranteed? And how can businesses leverage data while preserving privacy? This will be the topic of our first webinar, which we’ll be hosting on the 14th of April - you can sign up here. If you’re not sure whether this is for you, read on for an overview of the topic.
Privacy defines a state in which one is free from public attention and not observed or disturbed by others. Taken in the context of data, privacy is therefore a state in which an individual’s data is used only with their specific consent, and where any person or organization party to that individual’s data guarantee to prevent unauthorized disclosures or misuse of that information. Therefore, in order to protect the individual's privacy, strict regulations have already been introduced in many regions and countries worldwide, such as CCPA in California or GDPR in the EU and we can expect many more to come. Companies, organizations or institutions need to respect those regulations when gathering and processing personal data within those respective regions.
Even though the concept of data privacy should be fundamental to organizations, it must often be balanced against other business interests, which when coupled with lack of organizational knowledge can easily lead to a misuse of personal data. Such misuse can occur when data is analyzed for purposes outside of the scope for which consent was granted, or through sharing with vendors or other third parties. This is not an insignificant challenge for organizations - more than €466,000,000 fines have been handed down since GDPR came into effect, and even large, industry leading companies such as Google, Facebook, Marriott Hotels and British Airways have been fined.
If processing and sharing information means respecting such strict regulations, one could ask why collect, process and share data in the first place? In modern organizations, data represents an opportunity to make more informed decisions, and to build new products and revenue streams. According to the Boston Consulting group, 80% of the most innovative companies use data to create and drive value. Such value creation can take many forms: depending on their vertical, organizations can use data for cases including decision support, product development, customer experience personalization, research and intra- and inter-business collaboration. Data presents an opportunity that organizations cannot afford to miss out on, but it’s use is not without challenges.
One approach to solving this dichotomy is to make the data anonymous - i.e to remove relationships between the data to be used and any individual. While there are many approaches to making data safer to use, including K-anonymity and pseudonymization, in many cases they can not provide guarantees of privacy, and are therefore not compliant with modern data privacy laws (If you’re interested in an introduction to privacy, and of the various data anonymization techniques available, our CDO, Omar Ali Fdal recorded a presentation at PyData Berlin on this topic (he’ll also be present in the webinar, so if you have questions, join us!)).
At Statice, we approach this problem from a slightly different angle: we train machine learning models to learn statistical and structural information inherent in (sensitive) data sets, and then generate new, synthetic data that has no 1:1 links to the original data. Instead, the new synthetic data represents as much statistical content and structure as is possible, while providing a mathematically guaranteed preservation of privacy.
Have you run into challenges with data usage in your organization? Do you want to enable your teams to conduct privacy-preserving data science? Or do you want to build new data-driven revenue streams? Sign up for our webinar on the 14th of April to find out how your business can benefit from synthetic data and unlock the value of data.
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