Presentation on theme: "Data, Privacy, Security, and The Courts: Where Are We? And, How Do We Get Out Of Here? Richard Warner Chicago-Kent College of Law"— Presentation transcript:
Data, Privacy, Security, and The Courts: Where Are We? And, How Do We Get Out Of Here? Richard Warner Chicago-Kent College of Law email@example.com
We Live in the Age of Big Data “Big Data” refers to the acquisition and analysis of massive collections of information, collections so large that until recently the technology needed to analyze them did not exist. And more: Datafication Massive amounts of unstructured “messy” data Otherwise unnoticed patterns Indiscriminate collection Indefinite retention for unpredictable future uses
Massive Messy Data Big Data analysis requires collecting massive amounts of messy data. Messy data: The data is not in a uniform format as one would see in traditional database, it is not annotated (semantically tagged). A technological breakthroughs was to find ways to manipulate and analyze such data. Massive amounts: think of every tweet ever tweeted. They are all in the Library of Congress. 400 million tweets a day in 2013.
Patterns We Would Not Notice Big Data analytics can reveal important patterns that would otherwise go unnoticed. Taking the antidepressant Paxil together with the anti- cholesterol drug Pravachol could result in diabetic blood sugar levels. Discovered by (1) using a symptomatic footprint characteristic of very high blood sugar levels obtained by analyzing thirty years of reports in an FDA database, and (2) then finding that footprint in the Bing searches using an algorithm that detected statistically significant correlations. People taking both drugs also tended to enter search terms (“fatigue” and “headache,” for example) that constitute the symptomatic footprint.
Benefits of Big Data Big Data analytics can reveal important patterns that would otherwise go unnoticed. Taking the antidepressant Paxil together with the anti- cholesterol drug Pravachol could result in diabetic blood sugar levels. Discovered by (1) using a symptomatic footprint characteristic of very high blood sugar levels obtained by analyzing thirty years of reports in an FDA database, and (2) then finding that footprint in the Bing searches using an algorithm that detected statistically significant correlations. People taking both drugs also tended to enter search terms (“fatigue” and “headache,” for example) that constitute the symptomatic footprint.
Obama and Big Data “Aiming to make the most of the fast-growing volume of digital data, the Obama Administration today announced a Big Data Research and Development Initiative. By improving our ability to extract knowledge and insights from large and complex collections of digital data, the initiative promises to help solve some the Nation’s most pressing challenges.” Office of Science and Technology Policy, Obama Administration Unveils “Big Data” Initiative: Announces $200 Million In New R&D Investments (Executive Office of the President, March 29, 2012), http://www.whitehouse.gov/sites/default/files/microsites/ostp/big_da ta_press_release.pdf.
Institution Transparency requirements Government Internal sharing Consumers and others seeking services ReportingSurveillance Privacy and security requirements Investigation Foreign governments Voluntary information sharing, Reporting requirements InvestigationExternal sharing
Indiscriminate Collection Big Data typically involves collecting diverse types of data. “In an intelligence driven security model, the definition of ‘security data’ expands considerably. In this new model, security data encompasses any type of information that could contribute to a 360-degree view of the organization and its possible business risks.” Sam Curry et al., “Big Data Fuels Intelligence-Driven Security” (RSA, January 2013), 4, http://www.emc.com/collateral/industry-overview/big- data-fuels-intelligence-driven-security-io.pdf.
Indefinite Retention, Unpredictable Uses The information is typically retained for a long time to use in unpredictable ways. as the Pravochol/Paxil example illustrates. The example also illustrates the rationale: the discovery of patterns we might not otherwise notice.
Loss of Informational Privacy Informational privacy is the ability to determine for ourselves what information about us others collect and what they do with it. None of the developments just outlined can happen without a loss of control over our data.
We Lose Control, They Gain It Information aggregators Businesses Government Our data “We can determine where you work, how you spend your time, and with whom, and with 87% certainty where you'll be next Thursday at 5:35 p.m.”
Increased power to control from knowing our location data.
New Privacy Problems? Changed privacy problems. A particularly complex and difficult tradeoff problem takes center stage. Big Data presents a much wider range of both risks and benefits—from detecting drug interactions to reducing emergency room costs to improving police response times.
Privacy Advocates and Courts Privacy advocates insist that We adopt severe restrictions on data collection, use, and retention, and. that courts should see the invasion of privacy as a compensable harm. Courts Refuse to see a mere invasion of privacy as a compensable harm Do not curtailed massive data collection, and Rarely hold businesses liable for data breaches.
And the Rest of Us: What We Want More control over our information, but without giving up the advantages information processing secures: We are willing trade. Humphrey Taylor, Most People Are “Privacy Pragmatists” Who, While Concerned about Privacy, Will Sometimes Trade It Off for Other Benefit, T HE H ARRIS P OLL (2003). What is the current mechanism for making privacy tradeoffs? Government: Constitutional and statutory constraints on government surveillance. Dana Priest and William M. Arkin, Top Secret America: The Rise of the New American Security State. Private business: Notice and Choice.
What We Have—Contractually Realized Notice and Choice Consumer Business Advertising ecosystem Payment system Aggregators Government
The Dominant Paradigm It is well known that these claims are false. Even so, Notice and Choice dominates public policy in both the US and the EU. An unsympathetic but not entirely inapt analogy: The old joke about the drunk and the streetlight. Why do policy makers and privacy advocates continue to look under the streetlight of Notice and Choice when it is clear that consent is not there?
The Failure of Notice and Choice Notice and Choice fails to To ensure free and informed consent. To define an acceptable tradeoff between privacy and the benefits of information processing. I focus on the problems about informed consent.
Informed Consent Impossible Two features of the advertising system make it impossible for a Notice to contain enough information: Complexity, and Long-term data retention.
Complexity The specificity assumption: informed consent requires knowing specific detail about what happens with the one’s information. The advertising system is too complex for a Notice to provide the required detail.
The Wrong Tradeoff Why would individual decisions based on information available at the time somehow add up an acceptable tradeoff? Even if Notices could, per impossible, contain all relevant information, and even if all visitors read and understood Notices, they would not have the information they need. The information required to adequately balance the benefits and risks concerns complex society- wide consequences that unfold over a long period of time.
Data Restrictions Proponents of Notice and Choice insist on restrictions on data collection and use: The Federal Trade Commission: Companies should limit data collection to that which is consistent with the context of the transaction or the consumer’s relationship with the business implement reasonable restrictions on the retention of data and should dispose of it once the data has outlived the legitimate purpose for which it was collected.
How Do We Get Out of Here? Notice and Choice first. We—my co-author Robert Sloan and I—think what policy makers have missed is the role of informational norms, Norms that govern the collection, use, and distribution of information. What follows is a bare bones outline of the idea, for more Robert Sloan and Richard Warner, Unauthorized Access: The Crisis in Online Privacy and Security, July 2013, http://www.crcpress.com/product/isbn/9781439830130. http://www.crcpress.com/product/isbn/9781439830130
What Norms Can Do When informational norms govern online businesses data collection and use practices, website visitors give free and informed consent to acceptable tradeoffs. As long as the norms are consistent with our values. Call such norms “value-optimal.”
Informed Consent A visitor’s consent is informed if the visitor can make a reasonable evaluation of the risks and benefits of disclosing information. Suppose visitors know transactions are governed by value-optimal norms, then: they know that uses of the visitor’s information— both uses now and uses in the unpredictable future—will implement tradeoffs between privacy and competing goals that entirely consistent with their values.
Tradeoffs All informational norms—value-optimal and non- value-optimal alike—implement a tradeoff between privacy and competing concerns. They permit some information processing, and thus secure some of its benefits, but they protect privacy by allowing only certain processing. When the norm is value-optimal, the tradeoff it implements it is justified by visitors’ values. The tradeoff is acceptable in this sense.
The Lack of Norms Problem Rapid advances in technology have created many situations for which we lack relevant value- optimal informational norms. Two cases : (1) relevant norms exist, but they are not value- optimal; (2) relevant norms do not exist at all.
Now What About Privacy Harms? The norms approach works—if indirectly. We can reduce the risk of harm problem by reducing unauthorized access. Can we reduce it to the point that we can adequately address the remaining increased risk of harm through existing means—insurance and recovery from identity theft? Whether we can is a matter of norms appropriate product-risk norms for software. and appropriate service-risk norms for malware.