Scott Jordan
Department of Computer Science University of California, Irvine
  Privacy

We are concerned in this research project with the development of a future United States law on consumer privacy. We draw upon three recent privacy laws and regulations:

(I am pleased to have had the opportunity to contribute to the FCC's Order and to the CCPA.)

We believe that development of future laws or regulations should be grounded in:

  • an understanding of what personal information companies collect, how they use it, and with whom they share it,
  • an appreciation for the different paths that GDPR, the FCC Broadband Privacy Order, and CCPA have taken, and
  • a recognition of the strengths and weaknesses of each of these laws and regulations.

 

Deficiencies in Privacy Policies 

Development of a comprehensive legal privacy framework in the United States should be based on identification of the common deficiencies of privacy policies. We attempt to delineate deficiencies by critically analyzing the privacy policies of mobile apps, application suites, social networks, Internet Service Providers, and Internet-of-Things devices. Whereas many studies have examined readability of privacy policies, few have specifically identified the information that should be provided in privacy policies but is not. 

Privacy legislation invariably starts a definition of personally identifiable information. We find that privacy policies' definitions of personally identifiable information are far too restrictive, excluding information that does not itself identify a person but which can be used to reasonably identify a person, and excluding information paired with a device identifier which can be reasonably linked to a person. Legislation should define personally identifiable information to include such information, and should differentiate between information paired with a name versus information paired with a device identifier. 

Privacy legislation often excludes anonymous and de-identified information from notice and choice requirements. We find that privacy policies' descriptions of anonymous and de-identified information are far too broad, including information paired with advertising identifiers. Computer science has repeatedly demonstrated that such information is reasonably linkable. Legislation should define these categories of information to align with technological abilities. Legislation should also not exempt de-identified information from notice requirements, to increase transparency. 

Privacy legislation relies heavily on notice requirements. We find that, because privacy policies' disclosures of the uses of personal information are disconnected from their disclosures about the types of personal information collected, we are often unable to determine which types of information are used for which purposes. Often, we cannot determine whether location or web browsing history is used solely for functional purposes or also for advertising. Legislation should require the disclosure of the purposes for each type of personal information collected. 

We also find that, because privacy policies disclosures of sharing of personal information are disconnected from their disclosures about the types of personal information collected, we are often unable to determine which types of information are shared. Legislation should require the disclosure of the types of personal information shared. 

Finally, privacy legislation relies heavily on user choice. We find that free services often require the collection and sharing of personal information. As a result, users often have no choices. We find that whereas some paid services afford users a wide variety of choices, paid services in less competitive sectors often afford users few choices over use and sharing of personal information for purposes unrelated to the service. As a result, users are often unable to dictate which types of information they wish to allow to be shared, and which types they wish to allow to be used for advertising. Legislation should differentiate between take-it-or-leave it, opt-out, and opt-in approaches based on the type of use and on whether the information is shared. Congress should consider whether user choices should be affected by the presence of market power.

Deficiencies in the Disclosures of Privacy Policies and in User Choice  (w/ S. Narasimhan and J. Hong), Research Conference on Communication, Information and Internet Policy (TPRC), Washington, D.C, September 2021.         

 

Definitions of Personally Identifiable Information

The computer science literature on identification of people using personal information paints a wide spectrum, from aggregate information that doesn't contain information about individual people, to information that itself identifies a person. However, privacy laws and regulations often distinguish between only two types, often called personally identifiable information and de-identified information. We show that the collapse of this technological spectrum of identifiability into only two legal definitions results in the failure to encourage privacy-preserving practices. We propose a set of legal definitions that spans the spectrum. 

We start with anonymous information. Computer science has created anonymization algorithms, including differential privacy, that provide mathematical guarantees that a person cannot be identified. Although the California Consumer Privacy Act (CCPA) defines aggregate information, it treats aggregate information the same as de-identified information. We propose a definition of anonymous information based on the technological possibility of logical association of the information with other information. We argue for the exclusion of anonymous information from notice and consent requirements. 

We next consider de-identified information. Computer science has created de-identification algorithms, including generalization, that minimize (but not eliminate) the risk of re-identification. GDPR defines anonymous information but not de-identified information, and CCPA defines de-identified information but not anonymous information. The definitions do not align. We propose a definition of de-identified information based on the reasonableness of association with other information. We propose legal controls to protect against re-identification. We argue for the inclusion of de-identified information in notice requirements, but the exclusion of de-identified information from choice requirements. 

We next address the distinction between trackable and non-trackable information. Computer science has shown how one-time identifiers can be used to protect reasonably linkable information from being tracked over time. Although both GDPR and CCPA discuss profiling, neither formally defines it as a form of personal information, and thus both fail to adequately protect against it. We propose definitions of trackable information and non-trackable information based on the likelihood of association with information from other contexts. We propose a set of legal controls to protect against tracking. We argue for requiring stronger forms of user choice for trackable information, which will encourage the use of non-trackable information. 

Finally, we address the distinction between pseudonymous and reasonably identifiable information. Computer science has shown how pseudonyms can be used to reduce identification. Neither GDPR nor CCPA makes a distinction between pseudonymous and reasonable identifiable information. We propose definitions based on the reasonableness of identifiability of the information, and we propose a set of legal controls to protect against identification. We argue for requiring stronger forms of user choice for reasonably identifiable information, which will encourage the use of pseudonymous information. Our definitions of anonymous information, de-identified information, non-trackable information, trackable information, and reasonably identifiable information can replace the over-simplified distinction between personally identifiable information versus de-identified information. We hope that this full spectrum of definitions can be used in a comprehensive privacy law to tailor notice and consent requirements to the characteristics of each type of information.  

Aligning Legal Definitions of Personal Information with the Computer Science of Identifiability , Research Conference on Communication, Information and Internet Policy (TPRC), Washington, D.C, September 2021.         

 

A Comparison of Notice and Consent Requirements under GDPR, CCPA, and the FCC Broadband Privacy Order

We compare the notice and consent requirements of the three recent privacy regulations that are most likely to serve as the starting points for the creation of a comprehensive consumer privacy bill in the United States: the European General Data Protection Regulation, the California Consumer Privacy Act / California Privacy Rights Act, and the Federal Communications Commission's Broadband Privacy Order. 

We compare the scope of personal information under each regulation, including the test for identifiability and exclusions for de-identified information, and identify problems with their treatment of de-identified information and of pseudonymous information. 

We compare notice requirements, including the level of required detail and the resulting ability of consumers to understand the use and flow of their personal information, and identify deficiencies with consumers' ability to track the flow of their personal information. 

Finally, we compare consumer choices under each regulation, including when a consumer must agree to the use of their personal information in order to utilize a service or application, and find that none of the regulations take full advantage of the range of options, and thereby fail to disincentive tracking.

Strengths and Weaknesses of Notice and Consent Requirements under the GDPR, the CCPA/CPRA, and the FCC Broadband Privacy Order, draft paper       

 

 

Portions of this work were supported by the Herman P. & Sophia Taubman Foundation and by NSF. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. One print or electronic copy may be made for personal use only. Permission must be obtained from the copyright holder for systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in these papers for a fee or for commercial purposes, modification of the content of these papers, reprinting or republishing of this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, and to reuse any copyrighted component of this work in other works.

Scott Jordan   UCICSNetworked Systems