Operationalizing Privacy in a Big Data Context

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?

Characteristics of Trust in a Time of Big Data

Implications for Life in a Time of Big Data
Goals, Methods and Models, Dilemmas and Opportunities
Ann Racuya-Robbins
February 20160229 —Spring 2016
1. Big Data Goals for Life — Survival?
Today the world store of human life has grown greatly. It is not clear that any other form of life has increased as rapidly, except perhaps the microbes and other life that cohabitates on/in human life. This increase has brought with it many concurrent and emergent problems and opportunities for life, not only human but all life. These problems and opportunities have simultaneously brought to bear the limits of our creative capabilities in understanding human survival and the survival of life. Someones of us have yelled fire, and millions of people and their technology are looking for answers and understanding. Generally speaking this development is a good thing; on some level every life wants to survive and even flourish and thrive. The question and the context then becomes; Is our collective effort of gathering knowledge—data and information for the survival of life?
For now it is important not to be distracted nor to make too much of the differences in terminology here of data, information and knowledge, as if in our case, data is something fundamentally different from information and knowledge. It is not. It may be reasonable to point out that data and information are kinds of knowledge and/or contexts of knowledge without inferring that these contextual differences are greater than the common ground of knowledge. We could claim our subject to be Big Knowledge or Big Information. For now Big Data may suffice. Later there will be time and effort applied to pinning the technological details of our project.
What makes data, knowledge or information Big? A hundred years hence?
What makes data Big Data? This is a second motive for our work here. To be sure one cause is simply the increase in human life population. This increase has created an increase in the volume of knowledge from data collected. This is the first characteristic identified in the NBDPWG Volume One Definitions. Because the data/information/knowledge comes largely from and in association with life it is full of variety another characteristic of Big Data. Life is at every instance various and significant, unique and changeable. Variety is a form of knowledge that changes over time. Knowledge of life that changes over time can be a picture, a life pattern. Highly detailed life patterns that change over time identify and are in aspects individual lives. Because of the volume and variety of knowledge from data there is both an apparent and real need for speed and velocity to understand this volume and variety. This apparent and real need for speed and velocity is both an intuitive and practical pressure being placed on technology to manage Bigness. Of course bigness is a relative and changeable term. More on this later. For today it might be more precise to say that human life is trying to find a strategy and technology for bringing together in an intelligible way differences in the speed and velocity of knowledge creation.

Implicit
For Whom
For What
For When
Principles
Projected
For Whom
For What
For When
Principles

2. Living Methods and Models
The Role of Thinking
The Role of Reflection
The Role of Metaphor
and Mapping
The Role Security
The Role of Privacy

3. Dilemmas and Opportunities for Life
Concurrency, Simultaneity, Parallelism and the Scientific Method
Uncertainty
Is it obsolete as an organizing principle?
Provenance
What history? From when?
Ownership
Orchestration and Orchestrator
Governance and Government
Emergence
PII

Human Trust Experience is not spelled “Functional Model”

We need to intentionally build an id ecosystem that is transparent and open and teach each other how to use it.

We need to intentionally build an id ecosystem—a way for human beings to interact online—that is transparent and open and teach each other how to use it.

Data Marking, data tagging…just another dilemma falling into the metadata bucket. I think in general data tagging and data marking are desirable only from the RP et al point view not the “subject attributes” [sic] human users point of view and I don’t support it. But reasoning that human users can’t understand these concepts is groundless. Instead we need to a much better job of developing information systems using commonly understandable terms, functional models, what have you and letting the ecosystem emerge from the common ground. To wit I am submitting a Minority Function Model Report as a small step in that direction. This common ground needs to be aligned to universal human rights. The concepts being referred to here are not that hard to understand but the infrastructure now being built at IDESG may take a rocket scientist to translate and untangle. That is in part intentional. It doesn’t have to be this way. We need to intentionally build an id ecosystem that is transparent and open and teach each other how to use it.

From my perspective by encouraging individual users to do more “tagging” the server/organization that collects, uses, passes around that tagging simply has more information about that user…how they think…what they do. While problematic, difficult, and posing its own dilemmas for democratic countries, it stands to reason that intelligence and defense agencies would want to use such an approach. Is there something that is not relevant to know about the “enemy”?

Making attributes public doesn’t make them facts nor is the claim compelling that there is little of personal privacy significance. These issues are more or less about metadata. They need to come into public discussion. The IDESG is one of the places this discussion should happen. At last the 800lb metadata elephant is seeming a little less invisible.

For more visit AnnRacuya-Robbins,com