The 9/11 Playbook for Protecting Privacy


Adam Klein and Edward Felten at Politico: “Geolocation data—precise GPS coordinates or records of proximity to other devices, often collected by smartphone apps—is emerging as a critical tool for tracking potential spread. But other, more novel types of surveillance are already being contemplated for this first pandemic of the digital age. Body temperature readings from internet-connected thermometers are already being used at scale, but there are more exotic possibilities. Could smart-home devices be used to identify coughs of a timbre associated with Covid-19? Can facial recognition and remote temperature sensing be harnessed to identify likely carriers at a distance?

Weigh the benefits of each collection and use of data against the risks.

Each scenario will present a different level of privacy sensitivity, different collection mechanisms, different technical options affecting privacy, and varying potential value to health professionals, meaning there is no substitute for case-by-case judgment about whether the benefits of a particular use of data outweighs the risks.

The various ways to use location data, for example, present vastly different levels of concern for privacy. Aggregated location data, which combines many individualized location trails to show broader trends, is possible with few privacy risks, using methods that ensure no individual’s location trail is reconstructable from released data. For that reason, governments should not seek individualized location trails for any application where aggregated data would suffice—for example, analyzing travel trends to predict future epidemic hotspots.

If authorities need to trace the movements of identifiable people, their location trails should be obtained on the basis of an individualized showing. Gathering from companies the location trails for all users—as the Israeli government does, according to news reports—would raise far greater privacy concerns.

Establish clear rules for how data can be used, retained, and shared.

Once data is collected, the focus shifts to what the government can do with it. In counterterrorism programs, detailed rules seek to reduce the effect on individual privacy by limiting how different types of data can be used, stored, and shared.

The most basic safeguard is deleting data when it is no longer needed. Keeping data longer than needed unnecessarily exposes it to data breaches, leaks, and other potential privacy harms. Any individualized location tracking should cease, and the data should be deleted, once the individual no longer presents a danger to public health.

Poland’s new tracking app for those exposed to the coronavirus illustrates why reasonable limits are essential. The Polish government plans to retain location data collected by the app for six years. It is hard to see a public-health justification for keeping the data that long. But the story also illustrates well how a failure to consider users’ privacy can undermine a program’s efficacy: the app’s onerous terms led at least one Polish citizen to refuse to download it….(More)”.

The War on Coronavirus Is Also a War on Paperwork


Article by Cass Sunstein: “As part of the war on coronavirus, U.S. regulators are taking aggressive steps against “sludge” – paperwork burdens and bureaucratic obstacles. This new battle front is aimed at eliminating frictions, or administrative barriers, that have been badly hurting doctors, nurses, hospitals, patients, and beneficiaries of essential public and private programs. 

Increasingly used in behavioral science, the term sludge refers to everything from form-filling requirements to time spent waiting in line to rules mandating in-person interviews imposed by both private and public sectors. Sometimes those burdens are justified – as, for example, when the Social Security Administration takes steps to ensure that those who receive benefits actually qualify for them. But far too often, sludge is imposed with little thought about its potentially devastating impact.

The coronavirus pandemic is concentrating the bureaucratic mind – and leading to impressive and brisk reforms. Consider a few examples. 

Under the Supplemental Nutrition Assistance Program (formerly known as food stamps), would-be beneficiaries have had to complete interviews before they are approved for benefits. In late March, the Department of Agriculture waived that requirement – and now gives states “blanket approval” to give out benefits to people who are entitled to them.

Early last week, the Internal Revenue Service announced that in order to qualify for payments under the Families First Coronavirus Response Act, people would have to file tax returns – even if they are Social Security recipients who typically don’t do that. The sludge would have ensured that many people never got money to which they were legally entitled. Under public pressure, the Department of Treasury reversed course – and said that Social Security recipients would receive the money automatically.

Some of the most aggressive sludge reduction efforts have come from the Department of Health and Human Services. Paperwork, reporting and auditing requirements are being eliminated. Importantly, dozens of medical services can now be provided through “telehealth.” 

In the department’s own words, the government “is allowing telehealth to fulfill many face-to-face visit requirements for clinicians to see their patients in inpatient rehabilitation facilities, hospice and home health.” 

In addition, Medicare will now pay laboratory technicians to travel to people’s homes to collect specimens for testing – thus eliminating the need for people to travel to health-care facilities for tests (and risk exposure to themselves or others). There are many other examples….(More)”.

The significance of algorithmic selection for everyday life: The Case of Switzerland


University of Zurich: “This project empirically investigates the significance of automated algorithmic selection (AS) applications on the Internet for everyday life in Switzerland. It is the first countrywide, representative empirical study in the emerging interdisciplinary field of critical algorithm studies which assesses growing social, economic and political implications of algorithms in various life domains. The project is based on an innovative mix of methods comprising qualitative interviews and a representative Swiss online survey, combined with a passive metering (tracking) of Internet use.


  • Latzer, Michael
     / Festic, Noemi / Kappeler, Kiran (2020): Use and Assigned Relevance of Algorithmic-Selection Applications in Switzerland. Report 1 from the Project: The Significance of Algorithmic Selection for Everyday Life: The Case of Switzerland. Zurich: University of Zurich. http://mediachange.ch/research/algosig [forthcoming]
  • Latzer, Michael / Festic, Noemi / Kappeler, Kiran (2020): Awareness of Algorithmic Selection and Attitudes in Switzerland. Report 2 from the Project: The Significance of Algorithmic Selection for Everyday Life: The Case of Switzerland. Zurich: University of Zurich. http://mediachange.ch/research/algosig [forthcoming]
  • Latzer, Michael / Festic, Noemi / Kappeler, Kiran (2020): Awareness of Risks Related to Algorithmic Selection in Switzerland. Report 3 from the Project: The Significance of Algorithmic Selection for Everyday Life: The Case of Switzerland. Zurich: University of Zurich. http://mediachange.ch/research/algosig [forthcoming]
  • Latzer, Michael / Festic, Noemi / Kappeler, Kiran (2020): Coping Practices Related to Algorithmic Selection in Switzerland. Report 4 from the Project: The Significance of Algorithmic Selection for Everyday Life: The Case of Switzerland. Zurich: University of Zurich. http://mediachange.ch/research/algosig [forthcoming]…(More)”.

Synthetic data offers advanced privacy for the Census Bureau, business


Kate Kaye at IAPP: “In the early 2000s, internet accessibility made risks of exposing individuals from population demographic data more likely than ever. So, the U.S. Census Bureau turned to an emerging privacy approach: synthetic data.

Some argue the algorithmic techniques used to develop privacy-secure synthetic datasets go beyond traditional deidentification methods. Today, along with the Census Bureau, clinical researchers, autonomous vehicle system developers and banks use these fake datasets that mimic statistically valid data.

In many cases, synthetic data is built from existing data by filtering it through machine learning models. Real data representing real individuals flows in, and fake data mimicking individuals with corresponding characteristics flows out.

When data scientists at the Census Bureau began exploring synthetic data methods, adoption of the internet had made deidentified, open-source data on U.S. residents, their households and businesses more accessible than in the past.

Especially concerning, census-block-level information was now widely available. Because in rural areas, a census block could represent data associated with as few as one house, simply stripping names, addresses and phone numbers from that information might not be enough to prevent exposure of individuals.

“There was pretty widespread angst” among statisticians, said John Abowd, the bureau’s associate director for research and methodology and chief scientist. The hand-wringing led to a “gradual awakening” that prompted the agency to begin developing synthetic data methods, he said.

Synthetic data built from the real data preserves privacy while providing information that is still relevant for research purposes, Abowd said: “The basic idea is to try to get a model that accurately produces an image of the confidential data.”

The plan for the 2020 census is to produce a synthetic image of that original data. The bureau also produces On the Map, a web-based mapping and reporting application that provides synthetic data showing where workers are employed and where they live along with reports on age, earnings, industry distributions, race, ethnicity, educational attainment and sex.

Of course, the real census data is still locked away, too, Abowd said: “We have a copy and the national archives have a copy of the confidential microdata.”…(More)”.

Birth of Intelligence: From RNA to Artificial Intelligence


Book by Daeyeol Lee: “What is intelligence? How did it begin and evolve to human intelligence? Does a high level of biological intelligence require a complex brain? Can man-made machines be truly intelligent? Is AI fundamentally different from human intelligence? In Birth of Intelligence, distinguished neuroscientist Daeyeol Lee tackles these pressing fundamental issues. To better prepare for future society and its technology, including how the use of AI will impact our lives, it is essential to understand the biological root and limits of human intelligence. After systematically reviewing biological and computational underpinnings of decision making and intelligent behaviors, Birth of Intelligence proposes that true intelligence requires life…(More)”.

Experts warn of privacy risk as US uses GPS to fight coronavirus spread


Alex Hern at The Guardian: “A transatlantic divide on how to use location data to fight coronavirus risks highlights the lack of safeguards for Americans’ personal data, academics and data scientists have warned.

The US Centers for Disease Control and Prevention (CDC) has turned to data provided by the mobile advertising industry to analyse population movements in the midst of the pandemic.

Owing to a lack of systematic privacy protections in the US, data collected by advertising companies is often extremely detailed: companies with access to GPS location data, such as weather apps or some e-commerce sites, have been known to sell that data on for ad targeting purposes. That data provides much more granular information on the location and movement of individuals than the mobile network data received by the UK government from carriers including O2 and BT.

While both datasets track individuals at the collection level, GPS data is accurate to within five metres, according to Yves-Alexandre de Montjoye, a data scientist at Imperial College, while mobile network data is accurate to 0.1km² in city centres and much less in less dense areas – the difference between locating an individual to their street and to a specific room in their home…

But, warns de Montjoye, such data is never truly anonymous. “The original data is pseudonymised, yet it is quite easy to reidentify someone. Knowing where someone was is enough to reidentify them 95% of the time, using mobile phone data. So there’s the privacy concern: you need to process the pseudonymised data, but the pseudonymised data can be reidentified. Most of the time, if done properly, the aggregates are aggregated, and cannot be de-anonymised.”

The data scientist points to successful attempts to use location data in tracking outbreaks of malaria in Kenya or dengue in Pakistan as proof that location data has use in these situations, but warns that trust will be hurt if data collected for modelling purposes is then “surreptitiously used to crack down on individuals not respecting quarantines or kept and used for unrelated purposes”….(More)”.

The potential of Data Collaboratives for COVID19


Blog post by Stefaan Verhulst: “We live in almost unimaginable times. The spread of COVID-19 is a human tragedy and global crisis that will impact our communities for many years to come. The social and economic costs are huge and mounting, and they are already contributing to a global slowdown. Every day, the emerging pandemic reveals new vulnerabilities in various aspects of our economic, political and social lives. These include our vastly overstretched public health services, our dysfunctional political climate, and our fragile global supply chains and financial markets.

The unfolding crisis is also making shortcomings clear in another area: the way we re-use data responsibly. Although this aspect of the crisis has been less remarked upon than other, more obvious failures, those who work with data—and who have seen its potential to impact the public good—understand that we have failed to create the necessary governance and institutional structures that would allow us to harness data responsibly to halt or at least limit this pandemic. A recent article in Stat, an online journal dedicated to health news, characterized the COVID-19 outbreak as “a once-in-a-century evidence fiasco.” The article continues: 

“At a time when everyone needs better information, […] we lack reliable evidence on how many people have been infected with SARS-CoV-2 or who continue to become infected. Better information is needed to guide decisions and actions of monumental significance and to monitor their impact.” 

It doesn’t have to be this way, and these data challenges are not an excuse for inaction. As we explain in what follows, there is ample evidence that the re-use of data can help mitigate health pandemics. A robust (if somewhat unsystematized) body of knowledge could direct policymakers and others in their efforts. In the second part of this article, we outline eight steps that key stakeholders can and should take to better re-use data in the fight against COVID-19. In particular, we argue that more responsible data stewardship and increased use of data collaboratives are critical….(More)”. 

Mobile phone data and COVID-19: Missing an opportunity?


Paper by Nuria Oliver, et al: “This paper describes how mobile phone data can guide government and public health authorities in determining the best course of action to control the COVID-19 pandemic and in assessing the effectiveness of control measures such as physical distancing. It identifies key gaps and reasons why this kind of data is only scarcely used, although their value in similar epidemics has proven in a number of use cases. It presents ways to overcome these gaps and key recommendations for urgent action, most notably the establishment of mixed expert groups on national and regional level, and the inclusion and support of governments and public authorities early on. It is authored by a group of experienced data scientists, epidemiologists, demographers and representatives of mobile network operators who jointly put their work at the service of the global effort to combat the COVID-19 pandemic….(More)”.

Data Protection under SARS-CoV-2


GDPR Hub: “The sudden outbreak of cases of COVID-19-afflictions (“Corona-Virus”), which was declared a pandemic by the WHO affects data protection in various ways. Different data protection authorities published guidelines for employers and other parties involved in the processing of data related to the Corona-Virus (read more below).

The Corona-Virus has also given cause to the use of different technologies based on data collection and other data processing activities by the EU/EEA member states and private companies. These processing activities mostly focus on preventing and slowing the further spreading of the Corona-Virus and on monitoring the citizens’ abidance with governmental measures such as quarantine. Some of them are based on anonymous or anonymized data (like for statistics or movement patterns), but some proposals also revolved around personalized tracking.

At the moment, it is not easy to figure out, which processing activities are actually supposed to be conducted and which are only rumors. This page will therefore be adapted once certain processing activities have been confirmed. For now, this article does not assess the lawfulness of particular processing activities, but rather outlines the general conditions for data processing in connection with the Corona-Virus.

It must be noted that several activities – such as monitoring, if citizens comply with quarantine and stay indoors by watching at mobile phone locations – can be done without having to use personal data under Article 4(1) GDPR, if all necessary information can be derived from anonymised data. The GDPR does not apply to activities that only rely on anonymised data….(More)”.

Deliberative Mini-Publics as a Response to Populist Democratic Backsliding


Chapter by Oran Doyle and Rachael Walsh: “Populisms come in different forms, but all involve a political rhetoric that invokes the will of a unitary people to combat perceived constraints, whether economic, legal, or technocratic. In this chapter, our focus is democratic backsliding aided by populist rhetoric. Some have suggested deliberative democracy as a means to combat this form of populism. Deliberative democracy encourages and facilitates both consultation and contestation, emphasizing plurality of voices, the legitimacy of disagreement, and the imperative of reasoned persuasion. Its participatory and inclusive character has the potential to undermine the credibility of populists’ claims to speak for a unitary people. Ireland has been widely referenced in constitutionalism’s deliberative turn, given its recent integration of deliberative mini-publics into the constitutional amendment process.

Reviewing the Irish experience, we suggest that deliberative mini-publics are unlikely to reverse democratic backsliding. Populist rhetoric is fueled by the very measures intended to combat democratic backsliding: enhanced constitutional constraints merely illustrate how the will of the people is being thwarted. The virtues of Ireland’s experiment in deliberative democracy — citizen participation, integration with representative democracy, deliberation, balanced information, expertise — have all been criticized in ways that are at least consistent with populist narratives. The failure of such narratives to take hold in Ireland, we suggest, may be due to a political system that is already resistant to populist rhetoric, as well as a tradition of participatory constitutionalism. The experiment with deliberative mini-publics may have strengthened Ireland’s constitutional culture by reinforcing anti-populist features. But it cannot be assumed that this experience would be replicated in larger countries polarized along political, ethnic, or religious lines….(More)”.