@DXWorldExpo Authors: Zakia Bouachraoui, Yeshim Deniz, Liz McMillan, Elizabeth White, Pat Romanski

Related Topics: @DXWorldExpo, Microservices Expo, Containers Expo Blog, @CloudExpo, Cloud Security, SDN Journal

@DXWorldExpo: Blog Feed Post

Taming Big Data Location Transparency

Concern over big government surveillance and security vulnerabilities has reached global proportions

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?

Location Transparency Pic


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.


The post Taming Big Data Location Transparency appeared first on Application Security.

More Stories By Andy Thurai

Andy Thurai is Program Director for API, IoT and Connected Cloud with IBM, where he is responsible for solutionizing, strategizing, evangelizing, and providing thought leadership for those technologies. Prior to this role, he has held technology, architecture leadership and executive positions with Intel, Nortel, BMC, CSC, and L-1 Identity Solutions.

You can find more of his thoughts at www.thurai.net/blog or follow him on Twitter @AndyThurai.

DXWorldEXPO Digital Transformation Stories
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Enterprises are striving to become digital businesses for differentiated innovation and customer-centricity. Traditionally, they focused on digitizing processes and paper workflow. To be a disruptor and compete against new players, they need to gain insight into business data and innovate at scale. Cloud and cognitive technologies can help them leverage hidden data in SAP/ERP systems to fuel their businesses to accelerate digital transformation success.
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...