In our previous blog post Data privacy and security: a symbiotic relationship for SaaS environments we discussed how privacy and data complement each other. Today we will continue on that topic and discuss why privacy and data need to work in tandem.
As organizations continue to embrace digital transformation strategies and utilization of the cloud, they are increasingly leveraging data analytics and science to derive greater insights from their data to spur on innovation and productivity. Data science provides limitless possibilities when it comes to harnessing data for new insights that improve and even save countless lives. However, many of the issues that organizations are looking to solve require access to sensitive information – for example, medical histories, financial records or even data from smart phones, or other user devices.
Without privacy and ethics baked into data-science, efforts can raise privacy and other concerns: an algorithm could discriminate based on age and gender with individuals experiencing harms such as not being able to qualify for loans, or the exact whereabouts of children being made known to people beyond what parents consented to. Such issues can be addressed where ethics and privacy by design practices are adhered to.
Privacy by Design has many facets including:
- Safe data design, e.g., designing a common access layer to enforce policies and control data access.
- Privacy-centric design to detect for any biases or potential issues during the design process.
- Controls to allow end users to have more control over their data and how it’s being used.
- Helping to de aggregate data sets so that sensitive data sets cannot be attributed back to specific individuals, for examples introduce random sampling techniques.
As data science continues to advance and the benefits increase, so too will the need to partner with privacy to ensure a design that protects individual users. Data science when used correctly can help prevent privacy concerns via statistical analysis, simulations and historical observations. When secure access is added to the mix, it results in improved confidence in personal data handling and data quality in cloud applications.