protection of relations within large datasets
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Protection of Relations Within Large Datasets. Mgr. Boleslav Bobčík, T-Systems Czech Republic, a.s. Let’s Start With Basic Facts …. Assets : valuable data contained in information systems Two families of threats targeted at data : - PowerPoint PPT PresentationTRANSCRIPT
Protection of Relations Within Large Datasets 123.5.2013
Protection of RelationsWithin Large DatasetsMgr. Boleslav Bobčík, T-Systems Czech Republic, a.s.
Protection of Relations Within Large Datasets 2
Let’s Start With Basic Facts…
• Assets: valuable data contained in information systems
• Two families of threats targeted at data:• Active threats – modification, unauthorized
alteration, destruction• Passive threats – unauthorized copying,
eavesdropping, data leaks
• Concerns with data leak detection• Easy to create a copy of data• The original data are unaffected by copying23.5.2013
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Data And Their Context
• Isolated (standalone) data• Low value• Their occurrence in
information systems is rather rare
• Context of data• Relations between data
records: substantial part of assets’ value
• Reason for relational DBMS popularity
• Usual target of attackers 23.5.2013
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Information System VulnerabilitiesHow the Architects Imagine Things...
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Information System VulnerabilitiesHow the System Actually Looks...
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Information System Vulnerabilities – Exploited
• Sony PlayStation® Network• April 2011• External attacker• Stolen 77 million
records• Direct damage: $171
million• Indirect damage: ???
• Lessons learned?• SonyPictures.com data
breach• June 2011
• Goold Health Systems• January 2013• Loss of backup media
with patient data• 6000 Medicaid records
including personal and payment data
• Gatineau Townhall, Canada• January 2013• Loss of student loans
data• 583 thousands records
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Usual Approaches To Data Protection
• Securing the perimeter• Objective: prevent
access of unauthorized people
• Authentication/authorization
• Problems• Threat of rogue insiders• Data taken out of the
perimeter are „defenseless“
• Data encryption• Objective: protect
static representation of data
• Database-level encryption
• Data accessible only for authorized users
• Problems• Often „All-or-Nothing“
solution• Cryptographic key
management• Data recovery risks
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Alternative Approach
• Securing the relations between data• Idea (based on relational database theory)• Divide the data into „context domains“• Link the records across domain boundaries with
secure identifiers• Secure identifier construction• Initial data structure• Encrypted with domain-related key• Result: seemingly random sequence of bits• All identifier transformations performed in secure
environment
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Data Before Secure Identifier Application
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Data After Secure Identifier Application
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?
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... But We Can Go Further
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Aspects Of Successful Deployment
• Applications in legacy information systems• Invasive change,
impact depends on architecture of the IS
• Intentional break of normal relationship implementation• Unable to utilize
standard database query techniques
• Possible solutions: NoSQL technologies, proxy drivers
• Large datasets are necessary• Avoiding the brute-
force threats• Reduced data
throughput• Security level is a
compromise between data protection and other parameters (performance, price, ease of use…)
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Benefits Of Protected Relationships
• Data access control• Context domains have isolated data character• Easy to manage access to individual domains
• Secure identifier operations performed by a separate subsystem• Dependency between data and physical device
prevents data theft• Additional security layers can be included
• Breach recovery mechanism• Compromised identifiers can be replaced 23.5.2013
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Similar Approaches
• PCI/DSS• Data tokenization• Opaque (uninterpretable) values substituting
sensitive data
• Format-preserving Encryption• Less-known / rarely used method
• IS ORG – personal identifier translator• Internal component of Czech eGovernment system• No public interface
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Final Remarks
• Present and future trends• Advances in system integration – new
vulnerabilities• Cybercrime (esp. „identity theft“) on the rise• Increasing adversary professionalization (e.g.
Chinese PLA Unit 61398)• Data protection legislation (EU – „General Data
Protection Regulation“, expected adoption in 2014)
• Conclusion: new information systems should consider protection of the data as well as data relations• Secure identifier system is a useful part of the
security landscape
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