lated data of one individual isn’t big
data. 18 In contrast to Estrin’s work, we
do not restrict attention to any particular data vertical. In our case, inversely
private data of an individual tends to
be on the biggish side10—recall the story of Max Schrems described earlier in
this Viewpoint.
References
1. Acquisti, A., John, L., and Loewenstein, G. What is
privacy worth? In Privacy Papers for Policy Makers
2010; http://www.futureofprivacy.org/privacy-
papers-2010/
2. California S. B. 27. Shine the Light Law. Civil Code
x1798.83; http://goo.gl/zxuHUi
3. Estrin, D. What happens when each patient becomes
their own “universe” of unique medical data? TedMed
2013; http://www.tedmed.com/talks/show?id=17762
4. Estrin, D. Small data where n = me. Commun. ACM 57,
4 (Apr. 2014), 32–34.
5. Fair and Accurate Credit Transaction Act of 2003,
Public Law 108{159, 108th Congress, 117; http://goo.
gl/PnpCkv
6. Fair Credit Reporting Act (FCRA), 1970. Title 15 U.S.
Code, sec. 1681; http://www.ftc.gov/os/statutes/fcra.htm
7. Federal Trade Commission. Final Report of the F TC
Advisory Committee on Online Access and Security,
2000; http://govinfo.library.unt.edu/acoas/papers/
finalreport.htm
8. Federal Trade Commission. Protecting Consumer
Privacy in an Era of Rapid Change, March 2012; http://
tinyurl.com/p6r3cy4
9. Federal Trade Commission. Data Brokers: A Call for
Transparency and Accountability, May 2014; http://
alturl.com/dwwy5
10. Gurevich Y., Haiby N., Hudis E., Wing J.M. and Ziklik E.
Biggish: A solution for the inverse privacy problem.
Microsoft Research MSR-TR-2016-24 (May 2016);
http://research.microsoft.com/apps/pubs/default.
aspx?id=266411
11. Gurevich, Y. and Neeman, I. Infon logic: The
propositional case. ACM Transactions on Computation
Logic 12, 2, article 9 (Jan. 2011).
12. Leon, P.G. et al. Why People Are (Un)willing to Share
Information with Online Advertisers. Carnegie Mellon
University, Computer Science Technical Report CMU-ISR- 15-106, http://goo.gl/RkqkhJ
13. Or well, G. 1984. Secker and Warburg, London, 1949.
14. Schneier, B. Data and Goliath: The Hidden Battles to
Collect Your Data and Control Your World. Norton and
Company, 2015.
15. Strom, D. Deborah Estrin wants to (literally) open
source your life. I TWorld (May 24, 2013); http://goo.
gl/Jsd9CT
16. U.S. Department of Health, Education, and Welfare
(HE W). Records, Computers, and the Rights
of Citizens. Report of the Secretary’s Advisory
Committee on Automated Personal Data Systems
(the HE W report), July 1973; https://epic.org/privacy/
hew1973report/
17. Warren, S.D. and Brandeis, L.D. The Right to Privacy.
Harvard Law Review 4, 5 (1890), 193–220.
18. Wikipedia. Big data. (Aug. 2015); https://en.wikipedia.
org/?oldid=675136834
Yuri Gurevich ( gurevich@microsoft.com) is Principal
Researcher, Microsoft Research, and Professor Emeritus
at the University of Michigan.
Efim Hudis ( efimh@microsoft.com) is Principal Architect
at Microsoft, Seattle, WA.
Jeannette M. Wing ( wing@microsoft.com) is Corporate
Vice President of Microsoft Research, Redmond, WA.
Copyright held by authors.
FTC report. Here are some additional
laws and FTC reports of relevance:
˲ A 2000 report of an FTC Advisory
Committee on “providing online consumers reasonable access to personal
information collected from and about
them by domestic commercial Web
sites, and maintaining adequate security for that information” 7;
˲ The 2003 Fair and Accurate Credit
Transactions Act providing consumers
with annual free credit reports from
the three nationwide consumer credit
reporting companies; 5
˲ California’s “Shine the light” law
of 2003, according to which a business
cannot disclose your personal information secretly from you to a third party
for “direct marketing purposes” 2; and
˲ A 2014 FTC report that calls for
laws making data broker practices
more transparent and giving consumers greater control over their personal information. 9
Clearly the law favors transparency
and facilitates your access to your inverse private infons.
Market forces. The sticky point is
whether companies will share back
our personal information. This information is extremely valuable to them.
It gives them competitive advantages,
and so it may seem implausible that
companies will share it back. We contend that companies will share back
personal information because it will be
in their business interests.
Sharing back personal information
can be competitively advantageous
as well. Other things being equal,
wouldn’t you prefer to deal with a company that shares your personal infons
with you? We think so. Companies will
compete on (a) how much personal
data, collected and derived, is shared
back and (b) how convenient that data
is presented to customers.
The evolution toward sharing back
personal information seems slow. This
will change. Once some companies
start sharing back personal data as part
of their routine business, the competitive pressure will quickly force their
competitors to join in. The competitors will have little choice.
There is money to be made in solving
the inverse privacy problem. As shar-
ing back personal information gains
ground, the need will arise to mine large
amounts of customers’ personal data
on their behalf. The benefits of owning
and processing this data will grow, espe-
cially as the data involves financial and
quality-of-life domains. We anticipate
the emergence of a new market for com-
panies that compete in processing large
sets of private data for the benefits of
the data producers, that is, consumers.
The miners of personal data will
work on behalf of consumers and
compete on how helpful they are to
the customers, how trustworthy they
are. This emerging market will generate its own pressure on the personal
data holders and potentially might
find ways to benefit them as well. For
example, if you shop at some retailer R
your personal data miner M may show
you a separate webpage devoted to R,
suggest ways for you to save money
as you shop there, and show you how
R intends to improve your shopping
experience. The last part may even be
written by R, but—working on your
behalf—M may also suggest to you
better deals or shopping experiences
elsewhere. The retailer R will benefit if
it can beat the competition.
Better record keeping. Finally, technology can enhance people’s capacity
to take and keep records. For example,
your smartphone or wearable device
may eventually become a trusted and
universal recorder of many things you
do. Technology will help people maintain a personal diary effortlessly.
The project “Small Data” lead by
Deborah Estrin at Cornell Tech3
pioneers such an approach in the domain
of health. “Consider a new kind of
cloud-based app that would create a
picture of your health over time by continuously, securely, and privately analyzing the digital traces you generate
as you work, shop, sleep, eat, exercise,
and communicate.” 4
The “small” in “Small Data” reflects
the fact that the personal health-re-
There is money
to be made in
solving the inverse
privacy problem.
Watch the authors discuss
their work in this exclusive
Communications video.
http://cacm.acm.org/videos/
inverse-privacy