At the recent Misti Big Data Security conference many forms of securing big data were discussed from encrypting the entire big data pool to just encrypting the critical bits of data within the pool. On several of the talks there was general discussion on securing Hadoop as well as access to the pool of data. These security measures include RBAC, encryption of data in motion between hadoop nodes as well as tokenization or encryption on ingest of data. What was missing was greater control of who can access specific data once that data was in the pool. How could role based access controls by datum be put into effect? Is such protection too expensive given the time critical nature of analytics or are there other ways to implement datum security?
Articles Tagged with DataGuise
We are seeing more and more cloud-based big data solutions for security, business analysis, application performance management, and many other things we see the results of every day, from when we search on Google, Bing, etc., to the email we get from various marketing campaigns. We know that governments and many others are using big data, whether in a cloud form or on-premise form, to correlate various forms of data to determine who we are, where we going, what we are doing, how we are doing something, and sometimes why we are doing anything. So with all this data out there in the hands of ‘others’, how can privacy be achieved for the individual? We touched on this within the Internet of Things: Expectation of Privacy article, and within this we spoke about the handling of personal and identifiable information (PII).