May 22, 2015 NISO Patron Privacy in Digital Library and Information Systems http://www.niso.org/topics/tl/patron_privacy/

1. Patron data collected and
offered by publishers
NISO Patron Privacy Virtual Forum #3
May 22, 2015
Richard Entlich
Collection Analyst Librarian
Cornell University Library

2. The Landscape
 Publishers collect a variety of information
about user interaction with their systems
◦ Some is specifically at the request and for the
benefit of libraries, such as COUNTER
reports
◦ Some consists of proprietary reports of
various kinds (outside of COUNTER) are
provided by some publishers. Others will
provide such reports upon request.
◦ Some is to meet publisher objectives, such as
protecting intellectual property (e.g., to detect
“excessive downloads”), or for marketing

3. Understanding publisher data
collection activity
 Libraries don’t know the scope of data that
publishers are collecting about their
patrons’ use of licensed e-resources
 Publisher web site terms & conditions and
privacy policy statements provide some
information but don’t offer a complete
picture
 Looking at the data publishers are already
providing to libraries can provide some
insight

4. Examples of publisher provided
data about users
 Licensed e-journals
 Licensed e-books
 Web scale discovery systems

5. Licensed e-journals
 Full-text article downloads by month by IP
address, platform level
◦ Provided routinely by many publishers
 American Chemical Society
 Association for Computing Machinery
 IEEE
 Nature Publishing Group
 Royal Society of Chemistry
 … and many others
◦ Most publishers can provide such reports
upon request, even if they don’t offer them on
their administrative portals

6. Downloads by month by IP address,
platform level
◦ Very similar in design to the COUNTER JR1
report, except by IP instead of journal title
Full-text downloads for [Whatever] University
(by month by IP address)
IP Address Jan-2014 Feb-2014 Mar-2014 YTD total PDF total HTML total
123.124.125.126 3 6 8
17 17 0
123.124.125.127 6 15 17
38 38 0
123.124.125.128 9 20 22
51 51 0
123.124.125.129 12 25 27
64 40 24
123.124.125.130 15 8 10
33 33 0
123.124.125.131 9 16 14
39 39 0
123.124.125.132 9 15 29
53 53 0
Total 63 105 127 295 271 24

7. Downloads by IP, article level
 Combines highly granular demographic
and bibliographic data
 At least one third party analytics provider,
MPS, makes such a report an option for
publishers using its MPSInsight product
 Some publishers make the report
available to libraries

10. Full-text article downloads by
IP
 Data returned (for a one month period)
◦ Journal [journal title]
◦ DOI [Digital object identifier]
◦ Title [article title]
◦ Volume
◦ Issue
◦ IP Address [full IPv4 in dot-decimal notation]
◦ Total Successful Full-Text Article Requests

11. Licensed e-books
 Full-text page, section, or chapter
downloads by IP address, platform level
◦ Provided routinely by some publishers
◦ Very similar in appearance to the comparable
e-journal report
◦ Most publishers can provide such reports
upon request, even if they don’t offer them on
their administrative portals

14. Usage statistics with
authentication details
 Data returned (for a full calendar year)
Customer Number # of Hits
Collection Number of Pages Viewed
MiL EAN/ISBN Number of Pages Downloaded
Title [e-book title] Number of Pages Printed
Publisher Checkouts
Pub e-EAN/ISBN License
Hardcover EAN/ISBN Authentication / Login type
Paper EAN/ISBN Login Date
LC Subject Heading IP Address [full IPv4 in dot-decimal notation]
LC Class Session ID

15. Web scale discovery systems: query
details

21. IP addresses and usage data
 An IP address does not identify a person,
but comes uncomfortably close
 What level of bibliographic data is
acceptable to combine with IP addresses?
 Should publisher systems be retaining
such data or sharing it with libraries?
 Do libraries have the right to ask
publishers not to collect it? Retain it?
Share it? Sell it?

22. Library use for IP address data
◦ Platform level download counts by IP
address can be very useful
◦ IP addresses can be converted into
demographic categories and then
removed, allowing for demographic
analysis of licensed e-resource use (e.g.
at the college or department level)
◦ Under the widely used IP-authentication
model, publisher systems are the best
source of such data

23. Recommended reading
 Some publishers provided “too much
information”
 Some publishers declined to share IP-based
data with the library, citing privacy concerns
 The University of Virginia library significantly
altered its funding model for licensed e-
resources based on usage patterns that the IP
data revealedhttp://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1422&context=charl
eston