1. Making Privacy Possible: Research on Organizational Privacy Technology
Clare-Marie Karat, Carolyn Brodie, and John Karat
Privacy Enabling Technology Research
Security, Networking and Privacy (SNAP)
To replace the title / subtitle with your own:
Click on the title block -> select all the text by pressing Ctrl+A -> press Delete key -> type your own text
IBM Research
4/25/2019

2. The Many Views of Privacy
Individual
“I want to be alone”
“I don’t really care what you know”
“Keep this between you and me” (confidential)
Organizational
“What are the legal requirements”
“How can I manage information”
There has been more research focus on Individual than on Organizational issues in use of Personal Information (PI)
Protecting data at rest (e.g., encryption, annonymization) vs. Providing accountable control over use
Organizations need help from technology to provide reasonable policies and to enforce them
IBM Research
4/25/2019

3. Privacy Research Statement
Most organizations store PI data in heterogeneous server system environments.
Currently they do not have a unified way of defining or implementing a privacy policy that encompasses both web and legacy applications across the different server platforms.
This makes the management of PI data difficult for both enterprises and end users.
IBM Research
4/25/2019

4. Progress to Date
Identified Organizational Needs – Initial survey (51 participants) asking about “top privacy concerns and technology needs”
Established Scenarios - In-Depth follow up (13 participants) to identify data flow and architectural concepts for privacy technology (e.g., sticky policy)
Iterated on Designs - Scenario-based walkthrough sessions of the privacy management prototype (SPARCLE) with target users (2 design iterations, 22 participants)
Conducted Evaluations - Laboratory study examining methods for policy authoring (36 participants)
Developed Architecture - Ongoing technical feasibility analysis
IBM Research
4/25/2019

5. Identify Organizational Needs
Recruited 51 Participants from Industry and Government:
North America
Europe
Asia Pacific
Sent Participants Privacy Questionnaires by
Asked about Top Concerns, Desired Function, Current Activities
Analyzed Data by Industry (N=23) and Government (N=28)
Questionnaire Response Rate was Approximately 80% from Customers
IBM Research
4/25/2019

6. Top Privacy Concerns Expressed
Industry and government patterns of concerns similar
Industry more concerned about economic harm to brand
Government more concerned about privacy violations by users outside the organization
IBM Research
4/25/2019

7. Desired Privacy Functions
Similar pattern across industry and government
Desired policy/data portability
Looked for easy to use authoring environment
Wanted one solution for all organizational data
IBM Research
4/25/2019

8. Establish Scenarios with Customers
How would you describe your role regarding privacy?
Can you give us an example scenario of what happens to a piece of PI as it passes through your organization from the time it is first collected until you dispose of it?
What are the strengths and weaknesses of your organization's current processes (manual or automated)?
What additional privacy functionality does your organization need and how would you like this privacy functionality to fit into your business process?
Are there different privacy issues for Web and Legacy data?
Do you have any other privacy requirements that you would like to tell us about?
IBM Research
4/25/2019

9. Scenario Approach Developed Enriched Domain-Specific Scenarios
Combined scenarios gathered from customers into four domain-specific stereotypical scenarios in:
Healthcare, Banking/Finance, Travel/Entertainment, Government
Enriched Scenarios Reviewed with 17 Customers in and face-to-face sessions
Customers agreed the scenarios represent all steps involving PI in their industries very well
Conducted Component Analysis of each Step
Broke each scenario down into steps such that each step involves one type of PI use....
Used EPA Benchmark, Mapped Tactical, and Created Future Roadmap Privacy Solutions for Customers
Used Scenarios in Design Review Sessions with Customers
IBM Research
4/25/2019

10. Iterative Design of Privacy Enabling Technology
Focused on key privacy steps from previous analysis
Established interaction requirements and a customer-validated design of a highly usable and effective privacy management tool called SPARCLE (Server Privacy ARchitecture and CapabiLity Enablement). Scope:
Author policies
Connect policy definition to system entities (Implement)
Check policy compliance (Audit)
Iteratively designed and reviewed with customers
10 sessions with 22 target users over 2 design iterations
IBM Research
4/25/2019

11. What is a Privacy Policy Rule?
Privacy is not about a single absolute privacy rule – Context Specific Policies are involved
Policies have been found to have stable form: Who (data user) can see my (data subject) information (data element)
For what purposes (e.g., marketing, patient care)
To carry out what actions (e.g., distribute)
Under what conditions (e.g., lives in California)
With what obligations (e.g., data subject must be notified)
Key Question - How can organizations author, implement, and audit privacy policies without rewriting all applications?
IBM Research
4/25/2019

12. IBM Research
4/25/2019

13. Parsed Rule
Original Rule
Rule Elements
IBM Research
4/25/2019

14. Laboratory Privacy Policy Rule Authoring Study
Can we determine whether the Natural Language or Structured Entry method is better for policy authoring?
Examined performance of novice policy authors
36 knowledge workers
Provided 3 scenarios describing a desired privacy situation
Asked people to write policy rules using three methods
Unconstrained
Natural Language with a policy rule template
Structured entry from lists of elements
IBM Research
4/25/2019

15. Privacy Policy Rule Authoring: Preferences
Unconstrained policy authoring left participants unsure of their rules
Natural Language template seemed to provide good guidance
Structured Entry seemed equally satisfying
Lower score represents higher degree of satisfaction
IBM Research
4/25/2019

16. Privacy Policy Rule Authoring: Quality
Unconstrained authoring yielded low quality (% elements identified)
Natural Language and Structured Entry yielded good quality
Including both methods seems to be most promising direction
IBM Research
4/25/2019

17. Privacy Policy Creation Utility
Author
Privacy
Policy
Machine
Readable
Natural
Language
Transform
Visualization
Of Privacy
Implementation
Utility
Enforcement
Engine
Log
Internal
Privacy Audit
Privacy Policy Creation Utility
IBM Research
4/25/2019

18. Next Steps
Continue enrichment and testing of the prototype with target customers!
Exploring relationship to compliance issues
IBM Research
4/25/2019