This document contains a biography and credentials for Dr. Tyrone W A Grandison. It lists his educational background which includes a BSc, MSc, and PhD from various universities. It outlines his work experience including 10 years at IBM and current work for the White House. It provides details on his recognition and awards in computer science and engineering. It notes his publications record of over 100 papers and 47 patents.

1. Dr Tyrone W A Grandison

2. All opinions expressed herein are my own and do not reflect the
opinions of of anyone that I work with (or have worked with) or
any organization that am or have been affiliated with.

3. • Jamaican
Education
• BSc Hons Computer Studies, UWI-Mona.
• MSc Software Engineering, UWI-Mona
• PhD Computer Science, Imperial College –
London
• MBA Finance, IBM Academy
Experience
• 10 years leading Quest team at IBM
• 2 years working in startups
• 3 years running companies and consulting
• Now, working for the White House
Recognition
• Fellow, British Computer Society (BCS)
• Fellow, Healthcare Information and Management
Systems Society (HIMSS)
• Pioneer of the Year (2009), National Society of
Black Engineers (NSBE)
• IEEE Technical Achievement Award (2010) for
“Pioneering Contributions to Secure and Private
Data Management".
• Modern Day Technology Leader (2009), Minority in
Science Trailblazer (2010), Science Spectrum
Trailblazer (2012, 2013). Black Engineer of the
Year Award Board
• IBM Master Inventor
• Distinguished Engineer, Association of Computing
Machinery (ACM)
• Senior Member, Institute of Electrical and
Electronics Engineers (IEEE)
Record
• Over 100 technical papers, over 47 patents and 2
books.

4. • The Fundamentals
• Auditing
• Privacy
• Cloud Computing
• Why Do We Need A&P in
Clouds
• The Current State of the
World
• Potential Research Areas
• Guiding Principles
• Considerations
• Research Roadmap
• Task 1
• Task 2
• Starting Point
• Small step 1
• Other Steps
• Conclusion

5. The process of collecting and evaluating evidence to determine whether
a computer system safeguards assets, maintains data integrity, achieves
organizational goals effectively and consumes resources efficiently
- Information Systems Control and Audit, Ron Weber (1998).
generates examined
by
Audit Log/Trail
Auditor

6. An individual’s right to control, edit, manage, and delete information
about them[selves] and decide when, how, and to what extent
information is communicated to others
 Privacy and Freedom. Alan F. Westin. (1967).
My Data
create
I authorize my doctor to view my
test results for diagnosis purposes only
My insurance company
is not authorized
to see any of my data

7. Cloud computing is a model for enabling ubiquitous, convenient, on-
demand network access to a shared pool of configurable computing
resources (e.g., networks, servers, storage, applications, and services)
that can be rapidly provisioned and released with minimal management
effort or service provider interaction.
- NIST Special Publication 800-145, Mell & Grance (2011).

8.  Public Trust
Conjunctive not Disjunctive
 Forensics
 CyberThreats

9. Developer
Gmail User
Interested
Government
(Agency)
Blackhat
Startup
Cloud
infiltrates
compromises

10.  Currently, cloud clients trust too much
 Real-time detection of an attack only possible in simplest, most obvious
cases
 Real-time notification is the exception (when possible) not the rule
 Due to cloud delivery model and cloud deployment model, the artifact
that any particular person is using may be different.
 Cloudy specifics on cloud, e.g. location of instances, mechanisms in
place, etc.
 For advanced auditing scenarios, details of the cloud operations,
communications with clients and client-based cloud operations need to
be known

11. 1. Creating Privacy-Preserving Logs
 Assumes that the cloud user does not have full confidence in the
cloud provider or their affiliated ecosystem.
1. Enabling Auditing in a Privacy-Preserving Manner
 Assumes there is not complete trust in the auditor and the service
provider.

12.  Seamless:
 Integrate into the current mode of operation with minimal to no significant.
 Transparent:
 It should be clear to the cloud service user what the purpose of the mechanism is and when it
is functioning.
 Elastic:
 Be able to scale to dynamically handle the request loads placed on the cloud service provider.
 Low Impact:
 Inclusion of the mechanism should have a minor impact on the storage and performance of
the cloud environment.
 Verifiable:
 An independent third party should prove the veracity of the actions of the mechanism.

13.  The Mechanism Injection Point (MIP)
 The mechanism injection point refers to the location of the A&P controls. This is the location
where enforcement of the auditing and privacy rules will be performed and the
supplementary mechanisms, such as data structures are situated.
 The Nature of the Cloud Service Employed
 Cloud Model being used, i.e. Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS)
and Infrastructure-as-a-Service (IaaS), etc.
 The Transaction Attack Vector
 The transaction attack vector refers to the class of transactions that are evaluated in the
process of assessing a possible threat.
 There are two types of transaction attack vectors: Requests and Consequences.
 The Threat Determination Point
 The threat determination point refers to the location where the analysis of the recorded
privacy and audit events occurs, i.e. the location where breach detection and notification
happens.

14.  Create the big picture
 Identify the basic problems
 Efficient Auditing Mechanisms
 Time Synchronization of Logs
 Creating Processing-Friendly, Privacy-Preserving Data
 Processing of Encrypted Log Data
 Mechanisms for Basic Cloud Forensics
 Solve the core problems
 Scale up to the big picture

15. User Cloud Service Provider (CSP)
Privacy-PreservingAPI
Public Key Infrastructure
NativeAPI
Pseudonym
Request/
Consequence
Parser
Resources
…..
…..
…..
…..
…..
App1
Appn
Privacy-PreservingAPI
C2: signed API request, with user ID
C2: API response/consequence

16. Auditor
C1
C2
C3
Public Key Infrastructure
Cloud Service Provider (CSP)User

19. Data
Tables
2004-02…
2004-02…
Timestamp
publicTelemarketingJohnSelect …2
OursCurrentJaneSelect …1
RecipientPurposeUserQueryID
Query Audit Log
Database
Layer
Query with purpose, recipient
Generate audit record
for each query
Updates, inserts, deletes
Backlog
Database triggers track
updates to base tables
Audit
Database
Layer
Audit query
IDs of log queries having
accessed data specified by the
audit query
• Audits whether particular data has
been disclosed in violation of the
specified policies
• Audit expression specifies what
potential data disclosures need
monitoring
• Identifies logged queries that
accessed the specified data
• Analyze circumstances of the
violation
• Make necessary corrections to
procedures, policies, security

20. Jane complains to the department of Health and Human Services saying that she
had opted out of the doctor sharing her medical information with pharmaceutical
companies for marketing purposes
The doctor must now review disclosures
of Jane’s information in order to
understand the circumstances of the
disclosure, and take appropriate action
Sometime later, Jane receives
promotional literature from a
pharmaceutical company,
proposing over the counter
diabetes tests
Jane has not been feeling well and decides to consult her doctor
The doctor uncovers that Jane’s blood sugar level is high and suspects
diabetes

21. audit T.disease
from Customer C, Treatment T
where C.cid=T.pcid and C.name =‘Jane’
Who has accessed Jane’s disease information?

22. Given
A log of queries executed over a data system
An audit expression specifying sensitive data
Precisely identify
Those queries that accessed the data specified by the audit
expression

23.  “Candidate” query
 Logged query that accesses all columns specified by the audit expression
 “Indispensable” tuple (for a query)
 A tuple whose omission makes a difference to the result of a query
 “Suspicious” query
 A candidate query that shares an indispensable tuple with the audit
expression
Query Q: Addresses of people with diabetes
Audit A: Jane’s diagnosis
Jane’s tuple is indispensable for both;
hence query Q is“suspicious” with respect to A

24. s PA(s PQ(T ´ R´ S)) ¹j
))((
))((
STA
RTQ
AOA
QOQ
PC
PC




Theorem - A candidate query Q is suspicious with respect to an audit expression A iff:
The candidate query Q and the audit expression A are of the form:
Query Graph Modeler (QGM) rewrites Q and A into:
)))((("" SRTQAi PPQ 

25. Data
Tables
2004-02…
2004-02…
Timestamp
publicTelemarketingJohnSelect …2
OursCurrentJaneSelect …1
RecipientPurposeUserQueryID
Query Audit Log
Database
Layer
Query with purpose, recipient
Generate audit record
for each query
Updates, inserts, delete
Backlog
Database triggers track
updates to base tables
Audit
Database
Layer
Audit expression
IDs of log queries having
accessed data specified by the
audit query
Static analysis
Generate audit
query

26. ID Timestamp Query User Purpose Recipient
1 2004-02… Select … James Current Ours
2 2004-02… Select … John Telemarketing public
Query Log
Audit expression
Filter Queries
Candidate queries
Eliminate queries that could
not possibly have violated the
audit expression
Accomplished by examining
only the queries themselves
(i.e., without running the
queries)
OAQ CC 

27. Merge logged queries and audit expression into a single query graph
Customer
c, n, …, t
audit expression := T.p=C.c and C.n=
‘Jane’
T.s
Select := T.s=‘diabetes’ and T.p=C.c
C.n, C.a, C.z
C
C
Treatment
p, r, …, t
T
T

28. Customer
c, n, …, t
audit expression := X.n= ‘Jane’
‘Q1’
Select := T.s=‘diabetes’ and C.c=T.p
C.n
View of Customer (Treatment) is a temporal view at
the time of the query was executed
The audit expression now ranges over the logged
query. If the logged query is suspicious, the audit
query will output the id of the logged query
Treatment
p, r, ..., t
X
C
T

29. 0
50
100
150
200
250
5 20 35 50
# of versions per tuple
Time(minutes)
Composite
Simple
No Index
No Triggers
7x if all tuples are updates
3x if a single tuple is updated
Negligible
by using
Recovery
Log to build
Backlog tables

30. 1
10
100
1000
Time(msec.)
# versions per tuple
Simple-I
Simple-C
Composite-I
Composite-C

31. Time Synchronization of Logs
Processing of Encrypted Log Data

32. Complete initial solutions for basic problems
 Show their importance (in other domains)
 Integrate into bigger picture.
 Demonstrate applicability to cloud environment
 Partner with Cloud providers to prototype and iron out kinks.
Focus on Cloud Forensics
 Privacy-Preserving Protocols
 Chain of Evidence
 Authenticity
Iterate on initial vision given the current state.

33. This space has a lot of difficult (and fundamental)
problems.
These specific questions need more researchers
focusing on them
Applicable not only to privacy and auditing in clouds
Translate to fundamental impact to basic Computer
Systems Research.
This is just my view and should never be thought to
be complete and definitive.

34. Twitter: @tyrgr
Email: tgrandison@proficiencylabs.com