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.
Disaster waiting to happen
With big data or analytics/BI (Business Intelligence), processing location is the key as it determines regulatory and data protection law compliance requirements and risk, for example, from government surveillance. Location transparency includes geographic location of data centers and cluster nodes that store and process the sensitive personal information of users. While most of the Big Data providers are able to provide security for the storage and transmission of sensitive data, most implementations don’t provide location transparency or location contingent data processing.
Providing corporations and their target consumers with visibility into where and how their information is processed can establish and build trust. User power would increase as consumers are able to choose where their data is processed, or stored, as opposed to being at the mercy of the big corporations and data consolidators.
Once consumers become aware of this issue, specific location processing could become a positive service differentiator in a highly competitive market. Currently, big data/analytics processing is often purely a function of processing capability and availability. However, given processing location information and applicable regulations and data protection laws, one could envision rule driven big data/analytics where the location of processing of sensitive personal information is also a function of processing locations, user choices /consent options, and policies.
How can it be solved?
Given the multi node processing capabilities of Big Data, you should be able to choose where and how (such as what level of security) you will be processing certain data from certain users. Given today’s technology, it is possible to build more secure clouds (including using technologies that verify a known clean state that is free of malware and virus – such as Intel Trusted Execution Technology – TXT) and have some of the big data nodes process the data more securely from within such highly secure clouds.
Conceptually, GRC (Governance, Risk and Compliance) collects the location of data subjects and processing resources. GRC, armed with location information, policy rules, and data subject choices can drive the data collection gateway and routing to correctly route personal information from data subjects in compliance with policy rules, and data subject choices, taking into consideration the locations of both the data subject and processing resources, and the level of security of the processing resources. Data can be scrubbed and protected before entering a Hadoop cluster or for data leaks at the API level, mitigating PII exposure at the outset. Especially if you use technologies such as tokenization by Intel Expressway Tokenization Broker, you can scrub for the personal data without the need to modify your applications intrusively. The smart intelligent gateways such as Intel Expressway API Manager or Service Gateway can do a context/ user/ sensitive data/ policy based routing dynamically.
They may also specify their preferred location and level of security of processing, further enhancing privacy in the areas of access and participation. For example, a person in Germany participating in an online service that involves Big Data/Analytics, perhaps for targeted advertising, prefers for their data to be processed in Germany with a higher level of security. In this case the data center, or Hadoop cluster nodes, used for processing of their data is routed to be processed on a high security compute environment in Germany. Aside from this general example of citizens of a given nation preferring their data processed within their country, another example could include controversial services such as online gambling where data subjects around the world would prefer any processing of their sensitive personal information, including for big data / analytics, to occur in certain geographies where regulations and data protection laws are more compatible with the particular online service provided, and levels of processing security take into consideration the value of their particular data and associated risk.
We propose a data classification levels tagging scheme to enable routing, such as “highly secure processing, geo tag restricted, medium or none”. For example, data tagged “none” will be executed in the next available cluster regardless of the location in the fastest, cheapest possible way. This could also enable service providers to charge based on the classification level as well. For example, if you guarantee an enterprise grade secure processing then you can charge a high premium to go with that. A geo restricted labeling would make sure the processing happens within a specific country on geo (such as EU zone) location. History of data movement and processing can be audited, tracked, and tuned to fit specific needs.
We can also use this approach to enable the service provider to enforce the cleansing operation based on the location. For example, if it is processed somewhere that is not considered a higher security location, destroy the data objects and clean up any residues after the operation.
This is an enhancement we are proposing to our Big Data group. Subsequently, we hope to influence all versions of Big Data.