Disclosing big data

Minnesota Law Review

Volumn 99, Issue 2, 2014, Pages 535-583

  Mattioli, Michael ^a  

^a INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

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EID: 84922515049     PISSN: 00265535     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (211)

1. See generally IAN AYRES, SUPER CRUNCHERS 60-63 (2007);
2. STEPHEN BAKER, THE NUMERATI 98-99 (2008);
3. BILL FRANKS, TAMING THE BIG DATA TIDAL WAVE 20 (2012);
4. VIKTOR MAYER-SCHÖNBERGER & KENNETH CUKIER, BIG DATA: A REVOLUTION THAT WILL TRANSFORM HOW WE LIVE, WORK, AND THINK (2013);
5. ERIC SIEGEL, PREDICTIVE ANALYTICS: THE POWER TO PREDICT WHO WILL CLICK, BUY, LIE, OR DIE 2-3 (2013).
6. See, e.g., Oswaldo Trelles et al., Big Data, But Are We Ready?, 12 NATURE REV. GENETICS 224, 224 (2011);
7. Patrick Tucker, Has Big Data Made Anonymity Impossible?, MIT TECH. REV. (May 7, 2013), available at http://www.technologyreview.com/news/514351/has-big-data-made-anonymity-impossible;
8. Ken Terry, Big Data Analytics, INFORMATIONWEEK, Mar. 1, 2013, at 8.
9. See FRANKS, supra note 1, at 20;
10. JAMES MANYIKA ET AL., MCKINSEY GLOBAL INST., BIG DATA: THE NEXT FRONTIER FOR INNOVATION, COMPETITION, AND PRODUCTIVITY 11 (2011), available at http://www.mckinsey.com/insights/business-technology/big-data-the-next-frontier-for-innovation;
11. see also Press Release, Office of Sci. & Tech. Policy, Exec. Office of the President, Obama Administration Unveils "Big Data" Initiative: Announces $200 Million In New R&D Investments 1 (Mar. 29, 2012) [hereinafter 2013 White House Press Release] available at http://www.whitehouse.gov/sites/default/files/microsites/ostp/big-data-press-release-final-2.pdf.
12. see also Christine L. Borgman, The Conundrum of Sharing Research Data, 63 J. AM. SOC'Y FOR INFO. SCI. & TECH. 1059, 1059-60 (2012).
13. See, e.g., Declan Butler, When Google Got Flu Wrong, 494 NATURE 155, 155-56 (2013), available at http://www.nature.com/polopoly-fs/1.12413!/menu/main/topColumns/topLeftColumn/pdf/494155a.pdf;
14. Quentin Hardy, Why Big Data Is Not Truth, N.Y. TIMES BITS BLOG (June 1, 2013, 8:00 AM), http://bits.blogs.nytimes.com/2013/06/01/why-big-data-is-not-truth;
15. Ari Zoldan, More Data, More Problems: Is Big Data Always Right?, WIRED (May 10, 2013, 12:49 PM), http://www.wired.com/2013/05/more-data-more-problems-is-big-data-always-right;
16. cf. Borgman, supra note 5, at 1067-69.
17. See Omer Tene & Jules Polonetsky, Big Data for All: Privacy and User Control in the Age of Analytics, 11 NW. J. TECH. & INTELL. PROP. 239, 270-71 (2013).
18. See id.; see also NAT'L ACAD. OF SCI. ET AL., ENSURING THE INTEGRITY, ACCESSIBILITY, AND STEWARDSHIP OF RESEARCHING DATA IN THE DIGITAL AGE 41, 63 (2009).
19. See Mark A. Lemley, The Surprising Virtues of Treating Trade Secrets As IP Rights, 61 STAN. L. REV. 311, 332 (2008);
20. See, e.g., L. Gordon Crovitz, Why 'Big Data' Is a Big Deal, WALL ST. J., Mar. 25, 2013, at A15;
21. Chuck Raasch, 'It Powers My Life, ' USA TODAY, Dec. 13, 2012, at 1A;
22. Alexandra Stevenson, Big Data Fund, N.Y. TIMES, Oct. 18, 2013, at B5.
23. See, e.g., Data, Data Everywhere, THE ECONOMIST, Feb. 27, 2010, at 3;
24. Alissa Quart, Cover Story, The Body-Data Craze, NEWSWEEK, June 26, 2013;
25. Michael Specter, Climate by Numbers, THE NEW YORKER, Nov. 11, 2013, at 38.
26. See, e.g., Luciano Floridi, Big Data and Their Epistemological Challenge, 25 PHIL. & TECH. 435, 436 (2012) available at http://link.springer.com/content/pdf/10.1007%2Fs13347-012-0093-4.pdf;
27. Karen E.C. Levy, Relational Big Data, 66 STAN. L. REV. ONLINE 73, 73 n.3 (Sept. 3, 2013), http://www.stanfordlawreview.org/sites/default/files/online/topics/66-StanLRevOnline-73-Levy.pdf;
28. MAYER-SCHÖNBERGER & CUKIER, supra note 1, at 6.
29. See, e.g., MAYER-SCHÖNBERGER & CUKIER, supra note 1, at 6.
30. See, e.g., Tene & Polonetsky, supra note 7, at 243-51.
31. MAYER-SCHÖNBERGER & CUKIER, supra note 1, at 6.
32. Ryen W. White et al., Web-Scale Pharmacovigilance: Listening to Signals from the Crowd, 20 J. AM. MED. INFORMATICS ASS'N 404 (2013), available at http://jamia.bmj.com/content/20/3/404.full.pdf;
33. see also Stanford Ctr. for Internet & Soc'y, The Privacy Paradox-Health and Medical Privacy, YOUTUBE (Feb. 27, 2012, 00:32:24), http://www.youtube.com/watch?v=ntL4WMGkiXo.
34. See, e.g., Nicholas P. Tatonetti et al., Data-Driven Prediction of Drug Effects and Interactions, 4 SCI. TRANSLATIONAL MED., Mar. 14, 2012, at 1, 1-3, available at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3382018/pdf/nihms-373483.pdf;
35. see also CTR. FOR DRUG EVALUATION & RESEARCH, FOOD & DRUG ADMIN., GUIDANCE FOR INDUSTRY: DRUG INTERACTION STUDIES-STUDY DESIGN, DATA ANALYSIS, IMPLICATIONS FOR DOSING, AND LABELING RECOMMENDATIONS 2 (Feb. 2012), available at http://www.fda.gov/downloads/Drugs/GuidanceCompliance RegulatoryInformation/Guidances/ucm292362.pdf.
36. Altman, supra note 18;
37. White et al., supra note 18, at 1.
38. Altman, supra note 18;
39. White et al., supra note 18, at 1.
40. Bethany Percha et al., Discovery and Explanation of Drug-Drug Interactions Via Text Mining, 17 PAC. SYMP. ON BIOCOMPUTING 410, 411-13 (2012).
41. Altman, supra note 18;
42. White et al., supra note 18, at 1.
43. See, e.g., FRANKS, supra note 1, at 20-21;
44. MAYER-SCHÖNBERGER & CUKIER, supra note 1, at 45.
45. Lisa Gitelman & Virginia Jackson, Introduction, in "RAW DATA" IS AN OXYMORON 2 (Lisa Gitelman ed., 2013).
46. See, e.g., HUGH G. GAUCH, JR., SCIENTIFIC METHOD IN BRIEF 57 (2012).
47. See, e.g., MAYER-SCHÖNBERGER & CUKIER, supra note 1, at 44-45.
48. But see, e.g., GAUCH, supra note 24, at 57.
49. Martin White, Big Data-Big Challenges, ECONTENT (Nov. 9, 2011), http://www.econtentmag.com/Articles/Column/Eureka/Big-Data-Big-Challenges-78530.htm.
50. Tucker, supra note 2.
51. See Terry, supra note 2, at 8;
52. Trelles et al., supra note 2, at 224.
53. Paul Ohm, Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization, 57 UCLA L. Rev. 1701, 1703-05 (2010).
54. Paul Ohm, Response, The Underwhelming Benefits of Big Data, 161 U. PA. L. REV. ONLINE 339, 340 (2013), http://www.pennlawreview.com/online/161-U-Pa-L-Rev-Online-339.pdf.
55. Tene & Polonetsky, supra note 7, at 264;
56. Omer Tene & Jules Polonetsky, Privacy in the Age of Big Data: A Time for Big Decisions, 64 STAN. L. REV. ONLINE 63, 64-65 (2012), http://www.stanfordlawreview.org/sites/default/files/online/topics/64-SLRO-63-1.pdf.
57. Tene & Polonetsky, supra note 16, at 243-51;
58. Tene & Polonetsky, supra note 31, at 64-65.
59. Ohm, supra note 30, at 339-40.
60. See, e.g., MAYER-SCHÖNBERGER & CUKIER, supra note 1, at 5.
61. 2013 White House Press Release, supra note 3, at 1.
62. EXEC. OFFICE OF THE PRESIDENT, BIG DATA: SEIZING OPPORTUNITIES, PRESERVING VALUES 61 (May 2014), available at http://www.whitehouse.gov/sites/default/files/docs/big-data-privacy-report-5.1.14-final-print.pdf.
63. See White et al., supra note 18.
64. Oren Etzioni et al., To Buy or Not To Buy: Mining Airfare Data To Minimize Ticket Purchase Price, 9 ACM SIGKDD INT'L CONF. ON KNOWLEDGE DISCOVERY & DATA MINING 119-28 (2003), available at http://www.cis.temple.edu/∼yates/papers/hamlet-kdd03.pdf
65. cf. Charles Duhigg, How Companies Learn Your Secrets, N.Y. TIMES (Feb. 16, 2012), http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html
66. Flu Trends: How Does This Work, GOOGLE.ORG, http://www.google.org/flutrends/about/how.html (last visited Oct. 29, 2014)
67. cf. Predictive Policing, NAT'L INST. JUST., http://www.nij.gov/topics/law-enforcement/strategies/predictive-policing/pages/welcome.aspx. (last modified June 9, 2014)
68. See Borgman, supra note 5, at 1059.
69. See, e.g., Programme with Presentations, DIGITAL CURATION CONF., http://www.dcc.ac.uk/events/idcc14/programme-presentations (last visited Oct. 29, 2014).
70. Borgman, supra note 5, at 1059.
71. Limor Peer, Mind the Gap in Data Reuse: Sharing Data Is Necessary But Not Sufficient for Future Reuse, LONDON SCH. ECON. & POLI. SCI. (Mar. 28, 2014), http://blogs.lse.ac.uk/impactofsocialsciences/2014/03/28/mind-the-gap-in-data-reuse.
72. MAYER-SCHÖNBERGER & CUKIER, supra note 1, at 99;
73. AYRES, supra note 1, at 61-62.
74. See Michael J. Madison, Commons at the Intersection of Peer Productions, Citizen Science, and Big Data: Galaxy Zoo, in GOVERNING KNOWLEDGE COMMONS 209 (Brett M. Frischmann et al. eds. 2014);
75. see also Borgman, supra note 5, at 1070.
76. citing D. Butler, Mashups Mix Data Into Global Service: Is This the Future for Scientific Analysis?, 439 NATURE 6 (2006).
77. See NIST Big Data, NAT'L INST. STANDARDS & TECH. (June 7, 2013), http://bigdatawg.nist.gov.
78. See Keith Hare, Report of Study Group on Next Generation Analytics and Big Data, FARANCE INC. (June 5, 2013), http://www.jtc1sc32.org/doc/N2351-2400/32N2388b-report-SG-big-data-analytics.pdf;
79. Customer Experience Digital Data Community Group, W3C, http://www.w3.org/community/custexpdata (last visited Oct. 29, 2014).
80. See, e.g., Ajay Royyuru, IBM's Watson Takes on Brain Cancer, IBM RES., http://www.research.ibm.com/articles/genomics.shtml (last visited Oct. 29, 2014).
81. See, e.g., NAT'L ACAD. OF SCI. ET AL., supra note 8, at 34.
82. Danah Boyd & Kate Crawford, Six Provocations for Big Data 4 (Sept. 21, 2011), http://ssrn.com/abstract=1926431;
83. see also Hardy, supra note 6.
84. Kate Crawford, The Hidden Biases of Big Data, HARV. BUS. REV. BLOG (Apr. 1, 2013, 2:00 PM), http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data.
85. See, e.g., Nick Bilton, Disruptions: Data Without Context Tells a Misleading Story, N.Y. TIMES BITS BLOG (Feb. 24, 2013, 11:00 AM), http://bits.blogs.nytimes.com/2013/02/24/disruptions-google-flu-trends-shows-problems-of-big-data-without-context.
86. Borgman, supra note 5, at 1067.
87. Peer, supra note 42.
88. See Hardy, supra note 6.
89. see also Zoldan, supra note 6.
90. See, e.g., Editorial, Social Software, 4 NATURE METHODS 189 (2007), available at http://www.nature.com/nmeth/journal/v4/n3/full/nmeth0307-189.html (last visited Oct. 29, 2014);
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94. See AYRES, supra note 1, at 60.
95. Cf. Boyd & Crawford, supra note 49, at 6-7.
96. See Borgman, supra note 5, at 1071.
97. See Lemley, supra note 10, at 333.
98. See Peter S. Menell, The Challenges of Reforming Intellectual Property Protection for Computer Software, 94 COLUM. L. REV. 2644, 2652 (1994);
99. Mark A. Lemley & David W. O'Brien, Encouraging Software Reuse, 49 STAN. L. REV. 255, 258 (1997).
100. See UNIF. TRADE SECRETS ACT § 1(4), 14 U.L.A 538 (2005);
101. see also RESTATEMENT (THIRD) OF UNFAIR COMPETITION § 39 (1995).
102. Vincent Chiappetta, Myth, Chameleon or Intellectual Property Olympian? A Normative Framework Supporting Trade Secret Law, 8 GEO. MASON L. REV. 69, 76 (1999).
103. See, e.g., Lemley, supra note 10, at 317.
104. See RESTATEMENT (THIRD) OF UNFAIR COMPETITION §§ 44-45.
105. See VAN LINDBERG, INTELLECTUAL PROPERTY AND OPEN SOURCE: A PRACTICAL GUIDE TO PROTECTING CODE 130-31 (2008);
106. Indictment at 1-4, United States v. Aleynikov, 10 Crim. (S.D.N.Y. Feb. 11, 2010),
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109. Wendy Seltzer, Software Patents and/or Software Development, 78 BROOK. L. REV. 929, 981 (2013).
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112. see also Chiappetta, supra note 67, at 89-90.
113. See Samuelson et al., supra note 72, at 2409.
114. See, e.g., Chiappetta, supra note 67, at 89;
115. Lemley & O'Brien, supra note 65, at 276.
116. See J.H. Reichman, Computer Programs as Applied Scientific Know-How: Implications of Copyright Protection for Commercialized University Research, 42 VAND. L. REV. 639, 701 (1989).
117. See Lemley, supra note 10, at 333-34.
118. UNIF. TRADE SECRETS ACT § 1, 14 U.L.A. 538 (1985).
119. See also Wellogix, Inc. v. Accenture, LLP, 716 F.3d 867 (5th Cir. 2013).
120. See Ohm, supra note 29, at 1711.
121. See generally, Patent Act, 35 U.S.C. § 101 (2012);
122. State St. Bank & Trust Co. v. Signature Fin. Grp., Inc., 149 F.3d 1368 (Fed. Cir. 1998),
123. abrogated by In re Bilski, 545 F.3d 943 (Fed. Cir. 2008);
124. CLS Bank Int'l v. Alice Corp., 717 F.3d 1269 (Fed. Cir. 2013).
125. Patent Act, 35 U.S.C. § 112 (2012).
126. See Alice Corp. v. CLS Bank Int'l, 134 S. Ct. 2347 (2014).
127. Patent Act, 35 U.S.C. §§ 102-103.
128. In re Musgrave, 431 F.2d 882, 893 (C.C.P.A. 1970);
129. Datamize LLC v. Plumtree Software, Inc., 417 F.3d 1342, 1350 (Fed. Cir. 2005),
130. abrogated by Nautilus, Inc. v. Biosig Instruments, Inc., 134 S. Ct. 2120 (2014).
131. Nautilus, 134 S. Ct. at 2124.
132. Datamize, 417 F.3d at 1350.
133. See Exxon Research & Eng'g Co. v. United States, 265 F.3d 1371, 1375 (Fed. Cir. 2001).
134. David Friedman et al., Some Economics of Trade Secret Law, 5 J. ECON. PERSP., Winter 1991, at 61, 64.
135. Copyright Act, 17 U.S.C. § 102(b) (2012);
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137. Morrissey v. Procter & Gamble Co., 379 F.2d 675, 678 (1st Cir. 1967).
138. Morrissey, 379 F.2d at 678-79.
139. See, e.g., CCC Info. Servs., Inc. v. Maclean Hunter Mkt. Reports, Inc., F.3d 61, 67 (2d Cir. 1994);
140. Am. Dental Ass'n v. Delta Dental Plans Ass'n, 126 F.3d 977, 979 (7th Cir. 1997).
141. Copyright Act, 17 U.S.C. § 101 (2012);
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146. See Data Sources, DATASIFT, http://www.datasift.com/platform/datasources (last visited Oct. 29, 2014).
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151. Treato, http://www.treato.com (last visited Oct. 29, 2014).
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153. Telephone Interview with Anish Kattukaran, supra note 116.
154. See Ohm, supra note 29.
155. E-mail Exchange with Michael Bailey, supra note 125.
156. 45 C.F.R. § 164.514(e) (2013).
157. See, e.g., INFORMATION COMMISSIONER'S OFFICE, ANONYMISATION: MANAGING DATA PROTECTION RISK CODE OF PRACTICE 83-84 (Nov. 2012), [hereinafter CODE OF PRACTICE] available at http://ico.org.uk/for-organisations/data-protection/topic-guides/anonymisation.
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160. CancerLinQ, AM. SOC'Y OF CLINICAL ONCOLOGY, http://www.asco.org/ quality-guidelines/cancerlinq (last visited Oct. 29, 2014).
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163. BAKER, supra note 1, at 99, 121.
164. Telephone Interview with Anonymous Source #1, supra note 133.
165. BAKER, supra note 1, at 43-65.
166. Telephone Interview with Anish Kattukaran, supra note 116.
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168. Datamize, LLC v. Plumtree Software, Inc., 417 F.3d 1342, 1350 (Fed. Cir. 2005),
169. abrogated by Nautilus, Inc. v. Biosig Instruments, Inc., 134 S. Ct. 2120 (2014).
170. See Alice Corp. v. CLS Bank Int'l, 134 S. Ct. 2347 (2014).
171. Ohm, supra note 29.
172. Copyright Act, 17 U.S.C. § 101 (2012).
173. Key Publ'ns, Inc. v. Chinatown Today Publ'g Enters., Inc., 945 F.2d 509, 513 (2d Cir. 1991).
174. quoting U.S. COPYRIGHT OFFICE, GUIDELINES FOR REGISTRATION OF FACT BASED COMPILATIONS 1 (1989).
175. See Warren Publ'g, Inc. v. Microdos Data Corp., 115 F.3d 1509, 1520 (11th Cir. 1997);
176. BellSouth Adver. & Publ'g Corp. v. Donnelley Info. Publ'g, Inc., 999 F.2d 1436, 1441-42 (11th Cir. 1993);
177. Triangle Publ'ns, Inc. v. Sports Eye, Inc., 415 F. Supp. 682, 684-86 (E.D. Pa. 1976);
178. Jason R. Boyarski, The Heist of Feist: Protection for Collections of Information and the Possible Federalization of "Hot News," 21 CARDOZO L. REV. 871, 904 (1999).
179. See David E. Shipley, Thin but Not Anorexic: Copyright Protection for Compilations and Other Fact Works, 15 J. INTELL. PROP. L. 91, 141 (2007);
180. Julie Wald, Note, Legislating the Golden Rule: Achieving Comparable Protection Under the European Union Database Directive, 25 FORDHAM INT'L L.J. 987, 1016-17 (2002).
181. Am. Dental Ass'n v. Delta Dental Plans Ass'n, 126 F.3d 977, 979 (7th Cir. 1997).
182. E.g., ATC Distribution Grp., Inc. v. Whatever It Takes Transmissions & Parts, Inc., 402 F.3d 700, 707-08 (6th Cir. 2005).
183. Southco, Inc. v. Kanebridge Corp., 390 F.3d 276, 285-87 (3d Cir. 2004) (en banc).
184. Soc'y of Holy Transfiguration Monastery, Inc. v. Gregory, 689 F.3d 29, 52 (1st Cir. 2012);
185. see also Health Grades, Inc. v. Robert Wood Johnson Univ. Hosp., Inc., 634 F. Supp. 2d 1226, 1238 (D. Colo. 2009)
186. quoting Southco, 390 F.3d at 298.
187. Soc'y of Holy Transfiguration Monastary, 689 F.3d at 52.
188. BRETT FRISCHMANN, INFRASTRUCTURE: THE SOCIAL VALUE OF SHARED RESOURCES 263 (2012).
189. See FED. TRADE COMM'N, PROTECTING CONSUMER PRIVACY IN AN ERA OF RAPID CHANGE (2012), available at http://www.ftc.gov/sites/default/files/documents/reports/federal-trade-commission-report-protecting-consumer-privacy-era-rapid-change-recommendations/120326privacyreport.pdf.
190. see Yochai Benkler, Constitutional Bounds of Database Protection: The Role of Judicial Review in the Creation and Definition of Private Rights in Information, 15 BERKELEY TECH. L.J. 535, 543-45 (2000).
191. Patent Act, 35 U.S.C. § 103 (2012).
192. U.S. COPYRIGHT OFFICE, REPORT ON LEGAL PROTECTION FOR DATABASES 22 (1997), available at http://www.copyright.gov/reports/db4.pdf.
193. See The Consumer and Investor Access to Information Act of 1999: Hearing on H.R. 1858 Before the Subcomm. on Telecomms., Trade, & Consumer Prot. of the H. Comm. on Commerce, 106th Cong. 67-68 (1999).
194. Patent Act, 35 U.S.C. § 154(a).
195. Gideon Parchomovsky & Michael Mattioli, Partial Patents, 111 COLUM. L. REV. 207 (2011).
196. Patent Act, 35 U.S.C. §§ 112, 114.
197. Parchomovsky & Mattioli, supra note 196, at 208.
198. Cf. Hearing, supra note 194, at 67-68.
199. Consumer Access to Information Act of 2004, H.R. 3872, 108th Cong. (2004);
200. Database and Collections of Information Misappropriation Act, H.R. 3261, 108th Cong. (2003);
201. Collections of Information Antipiracy Act, H.R. 354, 106th Cong. (1999);
202. Consumer and Investor Access to Information Act, H.R. 1858, 106th Cong. (1999);
203. Collections of Information Antipiracy Act, H.R. 2652, 105th Cong. (1998);
204. Database Investment and Intellectual Property Antipiracy Act of 1996, H.R. 3531, 104th Cong. (1996).
205. E.g., Database Investment and Intellectual Property Antipiracy Act of 1996, H.R. 3531, 104th Cong. § 7 (1996).
206. See, e.g., 142 CONG. REC. 12, 483 (1996).
207. J.H. Reichman & Pamela Samuelson, Intellectual Property Rights in Data?, 50 VAND. L. REV. 51, 164 (1997).
208. Paula Baron, Back to the Future: Learning from the Past in the Database Debate, 62 OHIO ST. L.J. 879, 879 (2001);
209. see Reichman & Samuelson, supra note 204, at 75-76.
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211. U.S. PATENT & TRADEMARK OFFICE, MANUAL OF PATENT EXAMINING PROCEDURE § 2016 (9th ed. 2014).