Entity Date 1 Annie Shebanow 3/21/2014 2 Jackamo 3/21/2014 3 Peter Muhlberger 3/24/2014 4 ...

627 Pages·2014·13.16 MB·English

by  Annie Shebanow

Big Data RFI Index
# Entity Date
1 Annie Shebanow 3/21/2014
2 Jackamo 3/21/2014
3 Peter Muhlberger 3/24/2014
4 Consumer Action 3/25/2014
5 ARM and AMD  3/26/2014
6 USPIRG and Center for Digital Democracy 3/27/2014
7 Information Technology Industry Council 3/27/2014
8 Consumer Federation of America 3/28/2014
9 Leadership Conference on Civil and Human Rights et al. 3/28/2014
10 MAG-Net 3/28/2014
11 Abraham Wagner 3/28/2014
12 Mary Culnan 3/29/2014
13 Georgetown University 3/30/2014
14 Intrical 3/30/2014
15 Access 3/31/2014
16 ACLU 3/31/2014
17 Advertising Self‐Regulatory Council, Council of Better Business Bureaus 3/31/2014
18 Association for Computing Machinery 3/31/2014
19 Association of National Advertisers 3/31/2014
20 BSA | The Software Alliance 3/31/2014
21 CDT 3/31/2014
22 Center for Data Innovation 3/31/2014
23 Center for Digital Democracy 3/31/2014
24 Center for National Security Studies 3/31/2014
25 Cloud Security Alliance 3/31/2014
26 Common Sense Media 3/31/2014
27 Computer and Communications Industry Association  3/31/2014
28 Computing Community Consortium 3/31/2014
29 Consumer Watchdog 3/31/2014
30 Dell 3/31/2014
31 Direct Marketing Association  3/31/2014
32 Durrell Kapan 3/31/2014
33 Electronic Transactions Association 3/31/2014
34 Federation of American Societies for Experimental Biology 3/31/2014
35 Financial Services Roundtable  3/31/2014
36 Food Marketing Groups 3/31/2014
37 Future of Privacy Forum 3/31/2014
38 IMS Health 3/31/2014
# Entity Date
39 Interactive Advertising Bureau 3/31/2014
40 IT Law Group  3/31/2014
41 James Cooper 3/31/2014
42 Jason Kint 3/31/2014
43 Jonathan Sander, STEALTHbits 3/31/2014
44 Marketing Research Association  3/31/2014
45 McKenna Long & Aldridge  3/31/2014
46 Microsoft 3/31/2014
47 MITRE Corporation 3/31/2014
48 Mozilla 3/31/2014
49 NYU Center for Urban Science & Progress 3/31/2014
50 Pacific Northwest National Laboratory 3/31/2014
51 Privacy Coalition  3/31/2014
52 Reed Elsevier 3/31/2014
53 Frank Pasquale 3/31/2014
54 Sidley Austin 3/31/2014
55 Software & Information Industry Association  3/31/2014
56 TechAmerica 3/31/2014
57 TechFreedom 3/31/2014
58 Technology Policy Institute 3/31/2014
59 The Internet Association  3/31/2014
60 U.S. Chamber of Commerce  3/31/2014
61 US Leadership for the Revision of the 1967 Space Treaty 3/31/2014
62 VIPR Systems 3/31/2014
63 World Privacy Forum  3/31/2014
64 Constellation Research 4/2/2014
65 Fred Cate, Peter Cullen, and Viktor Mayer-Schönberger 4/4/2014
66 Healthcare Leadership Council  4/4/2014
67 Brennan Center for Justice  4/4/2014
68 Making Change at Walmart 4/4/2014
69 Online Trust Alliance 4/4/2014
70 MIT 4/4/2014
71 Coalition for Privacy and Free Trade  4/4/2014
72 EFF 4/4/2014
73 Privacy Coalition‐Updated 4/4/2014
74 EPIC 4/5/2014
75 Kaliya Identity Woman 1 4/5/2014
76 Kaliya Identity Woman 2 4/6/2014
# Entity Date
77 Tyrone Grandison 4/6/2014
78 Open Technology Institute, New America Foundation 4/8/2014
Annie Shebanow

My notes on Big Data RFI:

Privacy notices are difficult to draw and deliver for the data use. Policies are needed to safeguard data and
information. There should not be differences between the government and the private sectors for policy
frameworks or regulations on handling big data. Although we all know there are differences between
government and private sectors’ data use, the privacy policies should be general enough to apply to both
entities.

For any government or private organization:
 Big data policies needed for securely handling, storing, and protection of structure, unstructured,
and semi-structures data
 Privacy policy controls for big data must be part of organizations’ operations, and those privacy
policy controls should be public information and listed on the organizations’ Web sites. These
policies could be companies’ own policies and the regulatory environments policies. Transparency
is the key to safeguarding data privacy. These should be in bullet list format (not the large
documents written by attorneys in attorneys’ language, which no one reads them as some
companies provide during signup process.)
 Different privacy policies needed for the type of data use
 Differentiate big data analytics policies for collecting individual’s data directly (supplied by
individuals), indirectly from third parties (posts to social media, videos, sensor data), or public
records
 Global consortiums for big data analytics policies are needed to draft, modify standards and
policies, and accepts membership (U.N. of Big Data Analytics Policies)
 Countries’ memberships protects their citizens’ data, lack of membership may create economic
downfall. This will incentivize best practices for big data analytics policies.
 Special privacy policies for those countries that are not members of big data privacy consortiums
and when data is originated from those countries
 Fair information practices privacy policies needed for information sharing
 Big data privacy announcement policies needed for data and information sharing
 Random auditing policy for ensuring privacy policies’ of organizations (It could be similar of
SOX-404 process)
 Big data analytics policies whistleblower measures needed
 Policies needed to encourage big data analytics privacy policies education in our educational
systems for the understanding and protection of citizens’ fundamental rights and providing
information on existing data privacy policies and standards
From: Jackamo <
Sent: Friday, March 21, 2014 8:23 PM
To: [email protected]
Subject: Big Data & Big Brother
Dear Big Brother,
I know you love me and want to take care of me, that's why you want "Big Data" to love me too. But I don't
love you Big Brother or your friend Big Data. As a matter of fact Big Brother, you can take a flying bit-of-my-
ass and Big Data can choke on snot and slobber all over itself-just don't bother my right to be left alone.

I hope this doesn't seem impolite or rude Big Brother, but it seems to me you need to find something more
important to do; like finding a cure for clap or the common cold and stop wasting the taxpayers money on this
frivolous paranoid set of fantasies you've been having ad infinitum about monsters from the ID, etc. Maybe you
need a new therapist Big Brother? Maybe, you can discuss with him or her why you're so anal retentive and
obsessed about collecting other people's business. At any rate if, all else fails Big Brother: just relax, pour
yourself a favorite drink, then lay down and take it easy, and all those bad thoughts will go way-at least for a
while.
Love,
__JPC__
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From: Muhlberger, Peter <[email protected]>
Sent: Monday, March 24, 2014 2:55 PM
To: [email protected]
Subject: [Big Data RFI]
To contextualize my comments, a little about me: Till Oct. 2013, I was the National
Science Foundation's program director for cyber social sciences. This position
involved me in questions of cybersecurity and, thereby, data privacy. I am now in an
unrelated position, but remain interested in issues of big data privacy. My views
here are my own and not the views of the National Science Foundation. My background
includes political science, public policy, political psychology, and data intensive
social science.

I am still learning about the policy context of big data privacy. Consequently, what
I say here is an impression at this point. Nevertheless, it does seem that there are
some important points to be made. The following clarifies what I perceive to be
limitations of the current policy framework regarding big data and privacy issues,
and I sketch a more complete framework. I will address specific questions with
respect to this RFI (request for information) after elaborating this framework. I
comment on four of the five questions below, identifying them by question number.

It is my impression that policy makers are acutely aware of the potential social and
economic value of big data, but are less knowledgeable regarding the long-term
implications of big data analytics for individuals and society ('privacy') or the
related ethical implications. For instance, Leon Panetta, in the recent MIT / White-
House meeting on big data privacy discussed detailed examples of the benefits of big
data and, seemingly as an afterthought, mentioned that we should seek to achieve
these benefits while preserving our ethical mores with respect to privacy. He did
not mention what these mores are, how they bear on big data, or how they might be
preserved. The White House data privacy framework identifies privacy as a means to
achieving the trust needed to fully exploit big data, but does not elaborate on
privacy as an ethical ends or in terms of potential social implications.

The discussion, then, seems framed in terms of how to extract the benefits of big
data while providing some protection for a less than fully elaborated notion of
privacy. That notion of privacy seems to largely consist in avoiding alarm by the
public with respect to the spread of individually-identifiable data. Much attention
is also focused on technical solutions such as k-anonymity or differential privacy
that allow sharing of data--effectively, expanded use of big data--while making a bow
to privacy by preventing identification of individuals. This was much of the focus
of the MIT / White-House meeting.

A notion of privacy focused on the spread of individually identifiable data cannot
adequately capture the underlying issues behind privacy concerns. Privacy is only a
subset of a set of privacy-related concerns raised by big data that pose substantial
social and ethical risks. Technological solutions to insuring individuals are not
identified in big data are quite inadequate to address these risks. What is needed
is an expanded notion of risk and a robust regulatory regime, based on strong rights,
that continually incorporates input from the public.

First, it is not evident that technological solutions can even protect the simple
notion of privacy: that of anonymizing individual data. Syntactic models of
anonymity, such as k-anonymity and l-diversity, have been shown to be seriously
flawed because combining an anonymized dataset with other publicly-available datasets
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allows re-identification of large numbers of people in many circumstances. Big data
is precisely about integrating a diversity of datasets, but this then opens many
paths to identifying individuals. Differential privacy circumvents this problem by
not sharing anonymized datasets. Data is given to a curator who only answers
questions based on the data and the answers contain statistical noise to prevent
leakage of individual information. This works, however, only if the number of
related questions is constrained. That is, queries must be put on a
budget. Overall, this arrangement is far too onerous for organizations to willingly
adopt it for internal purposes. It means giving up control over a crucial resource,
making data difficult to analyze, and preventing resale to others, as well as
constraining uses that are best done with non-anonymized data, such as advertising.

Those organizations and persons most likely to use big data for the public good--
university researchers, non-partisan non-profits, and government agencies--are those
most likely to be potentially hamstrung by technical solutions to preserving
individual anonymity in data. Such organizations and persons are the least able to
protect themselves from onerous legal requirements with respect to data privacy.

Second, the social and ethical risks of big data are appreciably broader than a
narrow notion of privacy as individual anonymity. Some aspects of this are evident
from considering the functions that privacy is meant to serve. An older academic
literature on the social psychological functions of privacy, now largely ignored in
the more technology-focused privacy literature, discusses privacy as a crucial
element in a person's construction of their identity or sense of self. Privacy is
about limiting information about oneself so as to construct boundaries that create
the conditions for and also help define personal identities.

The need for privacy comes about, in substantial part, from the recognition that the
construction of personal identity is vulnerable to social influence. Other people
could influence, perhaps even control, the construction of personal identity if they
have access to sufficient private information. This information could allow them to
utilize means of social control--social shaming, reputational attacks, and
interpersonal manipulation--to alter the construction of personal identity and
otherwise impose harms, such as limiting future options.

Personal identity construction is not something merely confined to young people,
particularly in a society in which people are moving at an increasing pace into new
jobs, new roles more generally, and new geographic locations. Research in psychology
indicates that few adults have fully integrated identities, which means that they
accept alternate and often conflicting roles and identities. Numerous experimental
studies have found that framing issues in different ways, ways that often evoke
different identities and roles, elicits sharply different attitudes and decisions in
individuals.

In other words, many people have roles and identities that are sufficiently poorly
integrated that knowledgeable actors can readily manipulate them—cause them to act or
think in a way they would not if they knew as much as the manipulator. It is well
known that social identities powerfully affect political attitudes and
behavior. Such identities have apparently been used quite successfully to manipulate
the public in the political sphere. Various lines of psychological and sociological
research show that identities and roles guide much of day-to-day behavior as well —
as people make their way through the day by enacting such roles as 'parent,'
'employee,' 'consumer,' 'friend,' 'husband,' 'citizen,' and many others. Because
these roles and identities are less than perfectly defined and integrated in most
individuals, people are open to manipulation. An organization that could subtly
shift what a person understands by, for example, 'employee,' 'consumer,' 'citizen,'
or 'friend' or can affect when these roles are activated or the tradeoffs between
them could powerfully affect behavior.

2
Big data increasingly puts large organizations in the position of being sufficiently
knowledgeable about individuals or groups of individuals that they can effectively
impose social controls, particularly manipulation, on individuals. The social
science applications of big data and computationally intensive modeling and analysis
techniques are expanding rapidly. Today, advertisers build profiles of
individuals. In the not too distant future, social scientists may well be able to
take the sum total of what people have said and done online and done within view of
increasingly omnipresent sensor networks to construct detailed models of people's
beliefs and understandings as well as psychological, cognitive, and behavioral
propensities.

With such information, organizations will seek to determine what to say and do to
people to shift these people to a new belief system, personality, or identity
configuration that will prove beneficial to the organization manipulating that
person. 'Doing to' a person could be something as subtle as providing targeted
discounts for certain activities that shift behavior and self-definitions over the
long term. While for the most part organizations have thus far shied away from other
means of social control such as shaming and reputational attacks, such means could
come to be utilized in the absence of a clear and well-enforced regulatory regime
against them or given expanded possibilities to shame or attack people anonymously.

The prospects for capturing key aspects of people from social media and other data
and being able to use this information to greatly influence them beyond existing
levels of influence are, of course, speculative. Nevertheless, large companies have
put down multi-billion dollar wagers that big data about people can help them affect
their behavior. We also know that people who know us intimately can 'push our
buttons,' that most people are readily manipulated by psychological experiments, that
people seem to be manipulated at a substantial scale by political campaigns, and that
a person's daily activity and interaction contexts greatly influence behavior. Big
data is creating a world in which large organizations will likely have sufficient
access to background information about people to potentially know as much about them
as intimates, if not more. This is all the more possible to the extent that people
increasingly use social media and related contexts to interact with their friends and
to develop new identities and roles. These behaviors are permanently captured and
then shared between organizations. What remains is for natural language, text
mining, and other techniques to be further developed to make powerful inferences from
this vast quantity of information.

To the extent that good modeling of individual propensities will be possible in the
not too distant future, oligopolistic organizations could use such information to
more thoroughly influence people by presenting them with certain frames and belief
structures. This is already done in relatively poorly targeted ways today in
political campaigns. Organizations could also tamper with people's marginal costs of
activity—structuring their environments and behavioral affordances to influence
people's development. For example, social media companies could alter the
likelihoods of who people are likely to encounter online, to insure they have the
'right' friends to influence them in a particular direction. Prices, advertising,
and the availability of information (e.g., search engine top results) could be
modified to insure people encounter the 'right' information. Search companies
already personalize search results based on past behavior, and companies already seek
to influence the prices and information to which individuals are exposed.

The full set of such developments in technologies of manipulation remains
speculative, but such developments do seem to be in the realm of
possibility. Consequently, the question is whether to risk potentially substantial
social and ethical harm by allowing, as we currently are, the building of a vast
infrastructure by large organizations to accumulate and analyze big data about people
and to influence people's environments in various ways.

3
A technology of manipulation would, of course, be most effective if attuned to each
individual. Nevertheless, even without information about specific individuals, a
greatly improved technology of manipulation would still be possible with big data and
emerging computationally-intensive methods. Simply knowing, in vast quantity and
detail, the behavior and verbal expressions of highly specific types of individuals
should prove very useful in determining how to influence individuals of these
types. Thus, even if individuals were successfully anonymized in big data, by
technical or legal means, it would remain possible for organizations with access to
such data to use it to determine how to manipulate rather specific types of people,
which these organizations could identify without big data. Thus, the privacy of
individuals could be maintained even while individuals were being harmed. Because of
this, the ethical implications of big data should be construed more broadly than the
issue of individual privacy.

The solutions to the above concerns are apt to be complex and multi-faceted,
particularly with respect to the tradeoff between the potential social and private
value of big data and its potential harms. Nevertheless, any adequate solutions are
apt to embrace a number of key ethical principles that form a set of 'big data
rights': the right to know, the right to anonymity, the right to be forgotten, and
the right to be heard. Aspects of these principles also appear in the Consumer
Privacy Bill of Rights (CPBR). There are, however, a number of ways in which CPBR
would need to be fortified to be up to the task of fully addressing the potential
social and ethical harms of big data. First, the rights must be treated as strong
ethical rights. A strong right is not one that should be subject to definition and
voluntary self-restrictions by those entities with the most to gain in violating
those rights—organizations with large stakes in big data. The public and academia
should be most involved in defining these rights and monitoring their protection in a
changing environment. Second, people have a deep right to know—one that extends to
the inferences and profiles drawn about them and to exactly how these inferences and
profiles are being used to affect them.

People have a deep right to know what organizations know about them and how this
knowledge is being used with respect to them and what would be different in their
environment or information exposure were they not known to these organizations. For
example, people should have access to a service that would show them search results
that were withheld from them based on prior information held by a search firm or
commercial firm. They should also have access to a service that presents an
accessible summary of what organizations know about them, including inferences, and
access to organizations’ detailed information about them as well as information on
how the inferences organizations make about them are being used.

Once they know how their information is used, people need a practical mechanism to
help them articulate their concerns and have these concerns impressed upon policy
makers. That is, it is insufficient for people to only be able to express private
alarm about what they learn concerning the uses of big data; they need public fora
that allow them to articulate their concerns and relay collective concerns to policy
makers. This is a meaningful 'right to be heard.' Policy makers should welcome
continual input from the public with respect to something that could drastically and
not necessarily positively shape society.

People should have a default right to anonymity, balanced against the social benefits
of the data. Even when people are in public, they have the expectation that they are
not being tracked and analyzed--which is stalking. That is, people do not expect,
even in public places, that someone is recording their every move and making
inferences about those moves that add up to a detailed picture that may well invade
their privacy. Similarly, people do not expect their online activities to be
carefully tracked across websites, their utterances on social media sites to be
forever recorded, analyzed, and data or inferences shared with other
organizations. Until they obtain consent from affected individuals, organizations
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whose purposes are not entirely focused on the public good should not be allowed to
track and analyze individuals via big data methods. Perhaps such organizations could
persuade the public to make their data available by having a third option of
temporary tracking so the organizations could show people the benefits of their
maintaining information about them.

Such data and the consent to collect it should, however, be erasable at request as
well. This is the right to be forgotten. People seeking a new start, as many do,
were once able to move to a new town and begin anew, without others having
preconceptions about them. Identity development and change more generally are also
facilitated by a world in which others do not have perfect records of a person's
past. A right to be forgotten is therefore important. CPBR is thus far ambiguous
with respect to a right to be forgotten. Corporations are, for example, not held
responsible for data they do not control. This seems to allow that a company will
collect and share, with third parties, very substantial data about an individual,
which data is then out of the reach of a right to be forgotten because the company no
longer controls the third party data. A serious right to be forgotten would require
every piece of data collected by a company to receive a unique identifier that would
allow, with the right data systems, all instances of this data to be erased, even if
held by third parties. Moreover, not only should the data be erasable, but all
inferences built based on this data should be undone. These inferences pose the
greatest dangers to privacy.

Having presented a framework for understanding big data and privacy-related issues,
the specific questions from this RIF can be better addressed:

Question 1: The current U.S. policy framework is insufficient to fully address the
ethical and social implications of big data. Too much emphasis is currently placed
on technical solutions to insuring the privacy of individual data, which is not the
central issue with respect to the potentially adverse implications of big
data. Organizations such as information sector firms will likely not be required to
use the more effective technical solutions internally. The existing framework does
not capture the most important aspects of the functions of privacy--namely to protect
people who are vulnerable to manipulation of their identities, roles, and
beliefs. With such concerns in focus, the chief problem presented by big data shifts
to the many possibilities big data affords to large organizations for improving their
methods of influence. While the prospects for a vastly improved technology of
manipulation based on big data remain speculative, these prospects are not
implausible, companies are making big wagers that such technologies will prove
effective, and a risk-averse approach should be taken to minimize potential harms of
such advances. In particular, it is important to take seriously the notion that
people have strong rights with respect to privacy that supersede the economic benefit
of exploiting new technologies. Taking such rights seriously would require a far
more stringent regulatory scheme than is being currently considered.

Question 2: Given the potential for large-scale abuse of big data technologies,
there should be measures in place to insure that any organization that does not
operate exclusively in terms of the public interest, understood in a non-partisan
way, should be subject to constraints with respect to their big data capabilities,
particularly as outlined in the 'big data rights' above. Such rights implicitly call
for strong regulation of these organizations. In the case of organizations that
pursue the public good, rights should be balanced against potential for contributions
to the public welfare. Commercial organizations cannot be assumed to be exclusively
or even primarily concerned with the public good. Universities and government
agencies, on the other hand, are tasked with pursuing the common good and, ideally,
act under careful scrutiny and transparency rules, unlike commercial organizations.

Question 3: This question is part of the problem. Technological solutions to
insuring the privacy of individual data are not a sufficient answer. Instead,
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