Operationalizing Privacy in the Big Data Context
Ann Racuya-Robbins
Operationalizing privacy is largely about understanding the nature of the data you are interested in analyzing. Understanding of the nature of the data involves intuition, ethics and some ICT technical knowledge. An average adult person has the ability to understand and make decisions on these matters.
Once the nature of the data is understood intuitively, ethically and in a general technical way privacy requirements for Big Data can be delineated in alignment with national standards, laws and regulations followed by the further specification of technical details that meet privacy requirements in deployment. Importantly since this technical knowledge is based on intuitive and ethical parameters the average person can understand the relationships between the privacy parameters and the technical and they should remain clear.
Privacy Risk Assessment, Management, Prevention and Mitigation
Goal: Privacy Preserving Information Systems using Big Data, Big Data Analytics
Scoping the Privacy Context – A Question and Answer Tree.
Pre-Big Data Processing/Analytics also a Post-Big Data Processing/Analytics
QUESTION: The most important question is: Does your prospective data set(s) contain personal data*?
ANSWER: Yes.
FOLLOWUP QUESTION 1: How do you know?
FOLLOWUP ANSWER: Metadata Personal Data Tag
FOLLOWUP ANSWER: Provenance Report from Data Vendor or Agency
FOLLOWUP QUESTION 2: Can you verify? Reproduce?
FOLLOWUP ANSWER:
ANSWER: No.
FOLLOWUP QUESTION 1: How do you know?
FOLLOWUP ANSWER: Data Vendor Reports no Personal Data.
FOLLOWUP QUESTION 2: Can you verify?
FOLLOWUP ANSWER: No. Data Vendors maintains Proprietary Status of Data
ANSWER: I don’t know.
FOLLOWUP QUESTION 1: How can you find out?
QUESTION: Does your data set contain “raw” data?
QUESTION: How large is your Data Set(s) Cluster?
< 100 gig
< 1.5 TB
< 100 TB
etc
QUESTION: Will more than one data set be linked and analyzed.
QUESTION: What is the anticipated rate of arrival of the data? At what velocity will the Data Set(s) Cluster be processed/analyzed?
QUESTION: Is the data irregular and of multiple data types?
QUESTION: Will the processing/analytics be used for real-time decision-making?
More QUESTIONS to be determined.
*Appendix
Definitions TBD
Personal Data/Information
Data Actions
Collection
Processing
Use
Logging
Disclosure
Generation
Retention
Transformation
Transfer
Inference
Extrusion
Pollution
Manageability
Personal Data Metadata Tags
General Personal Data = PD-G
Very Sensitive Personal Data = PD-VS
Privacy Rights Risks, Harms and Mitigations (Controls)
Rights TBD
Harms
Appropriation: Personal information is used in ways that deny a person self-determination or fair value exchange.
Breach of Trust: Breach of implicit or explicit trusted relationship, including a breach of a confidential relationship
Distortion: The use or dissemination of inaccurate or misleadingly incomplete personal information
Exclusion: Denial of knowledge about or access to personal data. Includes denial of service.
Induced Disclosure: Pressure to divulge information.
Insecurity: Exposure to future harm, including tangible harms such as identity theft, stalking.
Loss of Liberty: Improper exposure to arrest or detainment.
Power Imbalance: Acquisition of personal information about person which creates an inappropriate power imbalance, or takes unfair advantage of or abuses a power imbalance between acquirer and the person.
Stigmatization: Personal information is linked to an actual identity in such a way as to create a stigma.
Surveillance: Collection or use, including tracking or monitoring of personal information that can create a restriction on free speech and/or other permissible activities.
Unanticipated Revelation: Non-contextual use of data reveals or exposes person or facets of a person in unexpected ways.
To Be Defined:
Data Inference
Extrusion
Pollution
Bias
Discrimination
Data Subjects Intellectual Property
Preventions, Mitigations, Controls
Big Data Guidelines Repository at WIPO or
