Taming Big Data Location Transparency

Andy Thurai, Chief Architect & CTO, Intel App security & Big Data (@AndyThurai) | David Houlding, Privacy Strategist, Intel (@DavidHoulding)

Original version of this article appeared on VentureBeat.

Concern over big government surveillance and security vulnerabilities has reached global proportions. Big data/analytics, government surveillance, online tracking, behavior profiling for advertising and other major tracking activity trends have elevated privacy risks and identity based attacks. This has prompted review and discussion of revoking or revising data protection laws governing trans-border data flow, such as EU Safe Harbor, Singapore government privacy laws, Canadian privacy laws, etc. Business impact to the cloud computing industry is projected to be as high as US $180B.

The net effect is that the need for privacy has emerged as a key decision factor for consumers and corporations alike. Data privacy and more importantly identity-protected, risk mitigated data processing are likely to further elevate in importance as major new privacy-sensitive technologies emerge. These include wearables, Internet of Things (IoT), APIs, and social media that powers both big data and analytics that further increase associated privacy risks and concerns. Brands that establish and build trust with users will be rewarded with market share, while those that repeatedly abuse user trust with privacy faux pas will see eroding user trust and market share. Providing transparency and protection to users’ data, regardless of how it is stored or processed, is key to establishing and building user trust. This can only happen if the providers are willing to provide this location and processing transparency to the corporations that are using them.

Big Data – Big Help or Big Risk?

By Andy Thurai (Twitter: @AndyThurai)

[Original shorter version of this article appeared on PW http://tiny.cc/gbiczw]

As promised in my last blog “Big Data, API, and IoT …..Newer technologies protected by older security” here is a deep dive on Big Data security and how to effortlessly secure Big Data effectively.

It is an unfortunate fact that like other open source models, Hadoop has followed a similar path in that it hasn’t focused that much on security.  “Project Rhino”, an Apache Hadoop security project initiative spearheaded by Intel is aimed at correcting the inherent deficits that previously made Hadoop an untenable solution for security conscious enterprises.

In order to effectively use Big Data, it needs to be secured properly. However if you try to force fit everything into an older security model with older security tools, you will undoubtedly end up compromising more than you think.