Category: Big Data Science

Privacy in a time of Big Data

Privacy in a Time of Big Data

Ann Racuya-Robbins

The emergence and existence of Big Data technologies and techniques have scoped the challenge of insuring privacy in contemporary life. It is fair to say that there is an inverse relationship, roughly speaking, between big data and privacy. That is as data scales up privacy challenges become more grave. The factors pressuring big data to scale, to get bigger, faster… are powerful including a hoped for competitive edge and speed and cost reduction of analytics to acquire these competitive edges under the name patterns. While the term patterns has gained currency in the field the term’s meaning is not so well understood. It is important to state clearly that the patterns that are sought are themselves data that contain or create an advantage. Understanding is itself an advantage.  By advantage is meant largely a competitive commercial monetary advantage by a third party other than the data subject.

Privacy is a subject of individual life and living.

Privacy is an expression of biologic specificity. Privacy properly ensured and governed preserves innovation, creativity and living development. In this way privacy is a key ingredient of survival and successful maturation.  The pervasion of data that has thrown open the loss of privacy carried in computer and ICT infrastructures is a relatively new phenomenon. The concern for privacy is a recognition of the broadening value of all individual life.  A recognition of the dignity and richness of every life. A recognition that individual life is not rightly an object or property of another.

Privacy cannot be reduced to personal information i.e. name, address and/or other factuals. PII is an obsolete moniker for our subject.

Let us stipulate that we will in this first instance be referring to living individual adults. Living has many stages and forms that must to be addressed later.

Privacy is—living individual’s control over and freedom and refuge from data collection, capture, extraction, surveillance, analytics, predictions, excessive persuasive practices and communication of the living individual’s life, including external or internal bodily functions, creations, conditions, behavior, social, political, familial and intimate interaction including mental, neural and microbial functioning—unless sanctioned by civil and criminal law and when sanctioned only under protocols where the ways and means of collection, capture, extraction, surveillance, analytics and communication including new methods to emerge are governed by appropriate social cooperation principles and safeguards embedded in ICT infrastructures and architectures overseen by democratic courts and civil and community organizations and individuals peers charged with insuring proper conduct.

Living individuals own the data generated by or from their lives. Should revenues be generated from the collection, capture, extraction, surveillance, analytics and communication of the living individual’s data the majority of revenue generated from the living individual’s life belong to the living individual. Data ownership, provenance, curation, governance as well as the consequences of violations of privacy practices must be encapsulated in or within the data, be auditable and travel in encrypted form with the data. Where possible block chain techniques shall be employed as well as counterfactual strategies (processes) in engineering privacy.

Provenance is an accounting of the history of data in an ICT setting.

 

Next Steps

Define further Data Governance, Data Provenance, Data Curation, Data Valuation. Integrate the principles and practices outlined above into an archetypal Privacy Use Case(s) and articulate the Privacy Use Case as it proceeds through the reference architecture.

 

 

 

 

Data Ownership in the Big Data Context

Who own the data in the Big Data context? What does ownership in the Big Data context mean?

 

Data Ownership –

Ann Racuya-Robbins 2016 06 10

Data ownership means that the data subject owns the majority of the revenues generated from data that emanates/ed from or was built upon the data subject’s data. A data subject is a living being. This kind of ownership would mean that the data subject has the authority over decisions including development and disposition of the data subject’s data.

Also see the Individuals Trust Frame Work

Big Data’s Dilemma for Democratic Governance

It seems to me that much of contemporary-government governance is built on the premise that statistical/computational methods are unbiased evidentiary approaches to informing many aspects of standards development, governance, policy and enforcement. In a democracy where differences exist this is particularly important because the best practice is to resolve differences through discussion and unbiased evidentiary information where people are encouraged to voluntarily come together even compromise. Big Data erodes if not undermines the fairness of the “unbiased evidentiary” basis of statistical/computational approaches. The lack of privacy in the Big Data setting is one of the expressions of this. This lack of privacy is a problem for both individuals and entities. It is really a collective problem of our time. Introducing noise into the computation has more or less as many drawbacks as benefits taken overall.

This is the conundrum I have been wrestling with and which I hope to shed some light on in the Implications for Life in a Time of Big Data Whitepaper I am working on in the NIST Big Data Public Working Group.

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