Practical approaches to big data privacy over time

Curated on Posted on by Stefaan Verhulst

Micah Altman, Alexandra Wood, David R O’Brien and Urs Gasser in International Data Privacy Law: “

  • Governments and businesses are increasingly collecting, analysing, and sharing detailed information about individuals over long periods of time.
  • Vast quantities of data from new sources and novel methods for large-scale data analysis promise to yield deeper understanding of human characteristics, behaviour, and relationships and advance the state of science, public policy, and innovation.
  • The collection and use of fine-grained personal data over time, at the same time, is associated with significant risks to individuals, groups, and society at large.
  • This article examines a range of long-term research studies in order to identify the characteristics that drive their unique sets of risks and benefits and the practices established to protect research data subjects from long-term privacy risks.
  • We find that many big data activities in government and industry settings have characteristics and risks similar to those of long-term research studies, but are subject to less oversight and control.
  • We argue that the risks posed by big data over time can best be understood as a function of temporal factors comprising age, period, and frequency and non-temporal factors such as population diversity, sample size, dimensionality, and intended analytic use.
  • Increasing complexity in any of these factors, individually or in combination, creates heightened risks that are not readily addressable through traditional de-identification and process controls.
  • We provide practical recommendations for big data privacy controls based on the risk factors present in a specific case and informed by recent insights from the state of the art and practice….(More)”.

Selected Readings on Data Responsibility, Refugees and Migration

Curated on Posted on by Andrew Young

By Kezia Paladina, Alexandra Shaw, Michelle Winowatan, Stefaan Verhulst, and Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of Data Collaboration for Migration was originally published in 2018.

Special thanks to Paul Currion whose data responsibility literature review gave us a headstart when developing the below. (Check out his article listed below on Refugee Identity)

The collection below is also meant to complement our article in the Stanford Social Innovation Review on Data Collaboration for Migration where we emphasize the need for a Data Responsibility Framework moving forward.

From climate change to politics to finance, there is growing recognition that some of the most intractable problems of our era are information problems. In recent years, the ongoing refugee crisis has increased the call for new data-driven approaches to address the many challenges and opportunities arising from migration. While data – including data from the private sector – holds significant potential value for informing analysis and targeted international and humanitarian response to (forced) migration, decision-makers often lack an actionable understanding of if, when and how data could be collected, processed, stored, analyzed, used, and shared in a responsible manner.

Data responsibility – including the responsibility to protect data and shield its subjects from harms, and the responsibility to leverage and share data when it can provide public value – is an emerging field seeking to go beyond just privacy concerns. The forced migration arena has a number of particularly important issues impacting responsible data approaches, including the risks of leveraging data regarding individuals fleeing a hostile or repressive government.

In this edition of the GovLab’s Selected Readings series, we examine the emerging literature on the data responsibility approaches in the refugee and forced migration space – part of an ongoing series focused on Data Responsibiltiy. The below reading list features annotated readings related to the Policy and Practice of data responsibility for refugees, and the specific responsibility challenges regarding Identity and Biometrics.

Data Responsibility and Refugees – Policy and Practice

International Organization for Migration (IOM) (2010) IOM Data Protection Manual. Geneva: IOM.

  • This IOM manual includes 13 data protection principles related to the following activities: lawful and fair collection, specified and legitimate purpose, data quality, consent, transfer to third parties, confidentiality, access and transparency, data security, retention and personal data, application of the principles, ownership of personal data, oversight, compliance and internal remedies (and exceptions).
  • For each principle, the IOM manual features targeted data protection guidelines, and templates and checklists are included to help foster practical application.

Norwegian Refugee Council (NRC) Internal Displacement Monitoring Centre / OCHA (eds.) (2008) Guidance on Profiling Internally Displaced Persons. Geneva: Inter-Agency Standing Committee.

  • This NRC document contains guidelines on gathering better data on Internally Displaced Persons (IDPs), based on country context.
  • IDP profile is defined as number of displaced persons, location, causes of displacement, patterns of displacement, and humanitarian needs among others.
  • It further states that collecting IDPs data is challenging and the current condition of IDPs data are hampering assistance programs.
  • Chapter I of the document explores the rationale for IDP profiling. Chapter II describes the who aspect of profiling: who IDPs are and common pitfalls in distinguishing them from other population groups. Chapter III describes the different methodologies that can be used in different contexts and suggesting some of the advantages and disadvantages of each, what kind of information is needed and when it is appropriate to profile.

United Nations High Commissioner for Refugees (UNHCR). Model agreement on the sharing of personal data with Governments in the context of hand-over of the refugee status determination process. Geneva: UNHCR.

  • This document from UNHCR provides a template of agreement guiding the sharing of data between a national government and UNHCR. The model agreement’s guidance is aimed at protecting the privacy and confidentiality of individual data while promoting improvements to service delivery for refugees.

United Nations High Commissioner for Refugees (UNHCR) (2015). Policy on the Protection of Personal Data of Persons of Concern to UNHCR. Geneva: UNHCR.

  • This policy outlines the rules and principles regarding the processing of personal data of persons engaged by UNHCR with the purpose of ensuring that the practice is consistent with UNGA’s regulation of computerized personal data files that was established to protect individuals’ data and privacy.
  • UNHCR require its personnel to apply the following principles when processing personal data: (i) Legitimate and fair processing (ii) Purpose specification (iii) Necessity and proportionality (iv) Accuracy (v) Respect for the rights of the data subject (vi) Confidentiality (vii) Security (viii) Accountability and supervision.

United Nations High Commissioner for Refugees (UNHCR) (2015) Privacy Impact Assessment of UNHCR Cash Based Interventions.

  • This impact assessment focuses on privacy issues related to financial assistance for refugees in the form of cash transfers. For international organizations like UNHCR to determine eligibility for cash assistance, data “aggregation, profiling, and social sorting techniques,” are often needed, leading a need for a responsible data approach.
  • This Privacy Impact Assessment (PIA) aims to identify the privacy risks posed by their program and seek to enhance safeguards that can mitigate those risks.
  • Key issues raised in the PIA involves the challenge of ensuring that individuals’ data will not be used for purposes other than those initially specified.

Data Responsibility in Identity and Biometrics

Bohlin, A. (2008) “Protection at the Cost of Privacy? A Study of the Biometric Registration of Refugees.” Lund: Faculty of Law of the University of Lund.

  • This 2008 study focuses on the systematic biometric registration of refugees conducted by UNHCR in refugee camps around the world, to understand whether enhancing the registration mechanism of refugees contributes to their protection and guarantee of human rights, or whether refugee registration exposes people to invasions of privacy.
  • Bohlin found that, at the time, UNHCR failed to put a proper safeguards in the case of data dissemination, exposing the refugees data to the risk of being misused. She goes on to suggest data protection regulations that could be put in place in order to protect refugees’ privacy.

Currion, Paul. (2018) “The Refugee Identity.” Medium.

  • Developed as part of a DFID-funded initiative, this essay considers Data Requirements for Service Delivery within Refugee Camps, with a particular focus on refugee identity.
  • Among other findings, Currion finds that since “the digitisation of aid has already begun…aid agencies must therefore pay more attention to the way in which identity systems affect the lives and livelihoods of the forcibly displaced, both positively and negatively.”
  • Currion argues that a Responsible Data approach, as opposed to a process defined by a Data Minimization principle, provides “useful guidelines,” but notes that data responsibility “still needs to be translated into organisational policy, then into institutional processes, and finally into operational practice.”

Farraj, A. (2010) “Refugees and the Biometric Future: The Impact of Biometrics on Refugees and Asylum Seekers.” Colum. Hum. Rts. L. Rev. 42 (2010): 891.

  • This article argues that biometrics help refugees and asylum seekers establish their identity, which is important for ensuring the protection of their rights and service delivery.
  • However, Farraj also describes several risks related to biometrics, such as, misidentification and misuse of data, leading to a need for proper approaches for the collection, storage, and utilization of the biometric information by government, international organizations, or other parties.  

GSMA (2017) Landscape Report: Mobile Money, Humanitarian Cash Transfers and Displaced Populations. London: GSMA.

  • This paper from GSMA seeks to evaluate how mobile technology can be helpful in refugee registration, cross-organizational data sharing, and service delivery processes.
  • One of its assessments is that the use of mobile money in a humanitarian context depends on the supporting regulatory environment that contributes to unlocking the true potential of mobile money. The examples include extension of SIM dormancy period to anticipate infrequent cash disbursements, ensuring that persons without identification are able to use the mobile money services, and so on.
  • Additionally, GMSA argues that mobile money will be most successful when there is an ecosystem to support other financial services such as remittances, airtime top-ups, savings, and bill payments. These services will be especially helpful in including displaced populations in development.

GSMA (2017) Refugees and Identity: Considerations for mobile-enabled registration and aid delivery. London: GSMA.

  • This paper emphasizes the importance of registration in the context of humanitarian emergency, because being registered and having a document that proves this registration is key in acquiring services and assistance.
  • Studying cases of Kenya and Iraq, the report concludes by providing three recommendations to improve mobile data collection and registration processes: 1) establish more flexible KYC for mobile money because where refugees are not able to meet existing requirements; 2) encourage interoperability and data sharing to avoid fragmented and duplicative registration management; and 3) build partnership and collaboration among governments, humanitarian organizations, and multinational corporations.

Jacobsen, Katja Lindskov (2015) “Experimentation in Humanitarian Locations: UNHCR and Biometric Registration of Afghan Refugees.” Security Dialogue, Vol 46 No. 2: 144–164.

  • In this article, Jacobsen studies the biometric registration of Afghan refugees, and considers how “humanitarian refugee biometrics produces digital refugees at risk of exposure to new forms of intrusion and insecurity.”

Jacobsen, Katja Lindskov (2017) “On Humanitarian Refugee Biometrics and New Forms of Intervention.” Journal of Intervention and Statebuilding, 1–23.

  • This article traces the evolution of the use of biometrics at the Office of the United Nations High Commissioner for Refugees (UNHCR) – moving from a few early pilot projects (in the early-to-mid-2000s) to the emergence of a policy in which biometric registration is considered a ‘strategic decision’.

Manby, Bronwen (2016) “Identification in the Context of Forced Displacement.” Washington DC: World Bank Group. Accessed August 21, 2017.

  • In this paper, Bronwen describes the consequences of not having an identity in a situation of forced displacement. It prevents displaced population from getting various services and creates higher chance of exploitation. It also lowers the effectiveness of humanitarian actions, as lacking identity prevents humanitarian organizations from delivering their services to the displaced populations.
  • Lack of identity can be both the consequence and and cause of forced displacement. People who have no identity can be considered illegal and risk being deported. At the same time, conflicts that lead to displacement can also result in loss of ID during travel.
  • The paper identifies different stakeholders and their interest in the case of identity and forced displacement, and finds that the biggest challenge for providing identity to refugees is the politics of identification and nationality.
  • Manby concludes that in order to address this challenge, there needs to be more effective coordination among governments, international organizations, and the private sector to come up with an alternative of providing identification and services to the displaced persons. She also argues that it is essential to ensure that national identification becomes a universal practice for states.

McClure, D. and Menchi, B. (2015). Challenges and the State of Play of Interoperability in Cash Transfer Programming. Geneva: UNHCR/World Vision International.

  • This report reviews the elements that contribute to the interoperability design for Cash Transfer Programming (CTP). The design framework offered here maps out these various features and also looks at the state of the problem and the state of play through a variety of use cases.
  • The study considers the current state of play and provides insights about the ways to address the multi-dimensionality of interoperability measures in increasingly complex ecosystems.     

NRC / International Human Rights Clinic (2016). Securing Status: Syrian refugees and the documentation of legal status, identity, and family relationships in Jordan.

  • This report examines Syrian refugees’ attempts to obtain identity cards and other forms of legally recognized documentation (mainly, Ministry of Interior Service Cards, or “new MoI cards”) in Jordan through the state’s Urban Verification Exercise (“UVE”). These MoI cards are significant because they allow Syrians to live outside of refugee camps and move freely about Jordan.
  • The text reviews the acquirement processes and the subsequent challenges and consequences that refugees face when unable to obtain documentation. Refugees can encounter issues ranging from lack of access to basic services to arrest, detention, forced relocation to camps and refoulement.  
  • Seventy-two Syrian refugee families in Jordan were interviewed in 2016 for this report and their experiences with obtaining MoI cards varied widely.

Office of Internal Oversight Services (2015). Audit of the operations in Jordan for the Office of the United Nations High Commissioner for Refugees. Report 2015/049. New York: UN.

  • This report documents the January 1, 2012 – March 31, 2014 audit of Jordanian operations, which is intended to ensure the effectiveness of the UNHCR Representation in the state.
  • The main goals of the Regional Response Plan for Syrian refugees included relieving the pressure on Jordanian services and resources while still maintaining protection for refugees.
  • The audit results concluded that the Representation was initially unsatisfactory, and the OIOS suggested several recommendations according to the two key controls which the Representation acknowledged. Those recommendations included:
    • Project management:
      • Providing training to staff involved in financial verification of partners supervise management
      • Revising standard operating procedure on cash based interventions
      • Establishing ways to ensure that appropriate criteria for payment of all types of costs to partners’ staff are included in partnership agreements
    • Regulatory framework:
      • Preparing annual need-based procurement plan and establishing adequate management oversight processes
      • Creating procedures for the assessment of renovation work in progress and issuing written change orders
      • Protecting data and ensuring timely consultation with the UNHCR Division of Financial and Administrative Management

UNHCR/WFP (2015). Joint Inspection of the Biometrics Identification System for Food Distribution in Kenya. Geneva: UNHCR/WFP.

  • This report outlines the partnership between the WFP and UNHCR in its effort to promote its biometric identification checking system to support food distribution in the Dadaab and Kakuma refugee camps in Kenya.
  • Both entities conducted a joint inspection mission in March 2015 and was considered an effective tool and a model for other country operations.
  • Still, 11 recommendations are proposed and responded to in this text to further improve the efficiency of the biometric system, including real-time evaluation of impact, need for automatic alerts, documentation of best practices, among others.

The Cambridge Handbook of Consumer Privacy

Curated on Posted on by Stefaan Verhulst

Handbook by Evan Selinger, Jules Polonetsky, and Omer Tene: “Businesses are rushing to collect personal data to fuel surging demand. Data enthusiasts claim personal information that’s obtained from the commercial internet, including mobile platforms, social networks, cloud computing, and connected devices, will unlock path-breaking innovation, including advanced data security. By contrast, regulators and activists contend that corporate data practices too often disempower consumers by creating privacy harms and related problems. As the Internet of Things matures and facial recognition, predictive analytics, big data, and wearable tracking grow in power, scale, and scope, a controversial ecosystem will exacerbate the acrimony over commercial data capture and analysis. The only productive way forward is to get a grip on the key problems right now and change the conversation….(More)”.

 

Lessons from Cambridge Analytica: one way to protect your data

Curated on Posted on by Stefaan Verhulst

Julia Apostle in the Financial Times: “The unsettling revelations about how data firm Cambridge Analytica surreptitiously exploited the personal information of Facebook users is yet another demoralising reminder of how much data has been amassed about us, and of how little control we have over it.

Unfortunately, the General Data Protection Regulation privacy laws that are coming into force across Europe — with more demanding consent, transparency and accountability requirements, backed by huge fines — may improve practices, but they will not change the governing paradigm: the law labels those who gather our data as “controllers”. We are merely “subjects”.

But if the past 20 years have taught us anything, it is that when business and legislators have been too slow to adapt to public demand — for goods and services that we did not even know we needed, such as Amazon, Uber and bitcoin — computer scientists have stepped in to fill the void. And so it appears that the realms of data privacy and security are deserving of some disruption. This might come in the form of “self-sovereign identity” systems.

The theory behind self-sovereign identity is that individuals should control the data elements that form the basis of their digital identities, and not centralised authorities such as governments and private companies. In the current online environment, we all have multiple log-ins, usernames, customer IDs and personal data spread across countless platforms and stored in myriad repositories.

Instead of this scattered approach, we should each possess the digital equivalent of a wallet that contains verified pieces of our identities. We can then choose which identification to share, with whom, and when. Self-sovereign identity systems are currently being developed.

They involve the creation of a unique and persistent identifier attributed to an individual (called a decentralised identity), which cannot be taken away. The systems use public/private key cryptography, which enables a user with a private key (a string of numbers) to share information with unlimited recipients who can access the encrypted data if they possess a corresponding public key.

The systems also rely on decentralised ledger applications like blockchain. While key cryptography has been around for a long time, it is the development of decentralised ledger technology, which also supports the trading of cryptocurrencies without the involvement of intermediaries, that will allow self-sovereign identity systems to take off. The potential uses for decentralised identity are legion and small-scale implementation is already happening. The Swiss municipality of Zug started using a decentralised identity system called uPort last year, to allow residents access to certain government services. The municipality announced it will also use the system for voting this spring….

Decentralised identity is more difficult to access and therefore there is less financial incentive for hackers to try. Self-sovereign identity systems could eliminate many of our data privacy concerns while empowering individuals in the online world and turning the established data order on its head. But the success of the technology depends on its widespread adoption….(More)“

Artificial Intelligence and the Need for Data Fairness in the Global South

Curated on Posted on by Stefaan Verhulst

Medium blog by Yasodara Cordova: “…The data collected by industry represents AI opportunities for governments, to improve their services through innovation. Data-based intelligence promises to increase the efficiency of resource management by improving transparency, logistics, social welfare distribution — and virtually every government service. E-government enthusiasm took of with the realization of the possible applications, such as using AI to fight corruption by automating the fraud-tracking capabilities of cost-control tools. Controversially, the AI enthusiasm has spread to the distribution of social benefits, optimization of tax oversight and control, credit scoring systems, crime prediction systems, and other applications based in personal and sensitive data collection, especially in countries that do not have comprehensive privacy protections.

There are so many potential applications, society may operate very differently in ten years when the “datafixation” has advanced beyond citizen data and into other applications such as energy and natural resource management. However, many countries in the Global South are not being given necessary access to their countries’ own data.

Useful data are everywhere, but only some can take advantage. Beyond smartphones, data can be collected from IoT components in common spaces. Not restricted to urban spaces, data collection includes rural technology like sensors installed in tractors. However, even when the information is related to issues of public importance in developing countries —like data taken from road mesh or vital resources like water and land — it stays hidden under contract rules and public citizens cannot access, and therefore take benefit, from it. This arrangement keeps the public uninformed about their country’s operations. The data collection and distribution frameworks are not built towards healthy partnerships between industry and government preventing countries from realizing the potential outlined in the previous paragraph.

The data necessary to the development of better cities, public policies, and common interest cannot be leveraged if kept in closed silos, yet access often costs more than is justifiable. Data are a primordial resource to all stages of new technology, especially tech adoption and integration, so the necessary long term investment in innovation needs a common ground to start with. The mismatch between the pace of the data collection among big established companies and small, new, and local businesses will likely increase with time, assuming no regulation is introduced for equal access to collected data….

Currently, data independence remains restricted to discussions on the technological infrastructure that supports data extraction. Privacy discussions focus on personal data rather than the digital accumulation of strategic data in closed silos — a necessary discussion not yet addressed. The national interest of data is not being addressed in a framework of economic and social fairness. Access to data, from a policy-making standpoint, needs to find a balance between the extremes of public, open access and limited, commercial use.

A final, but important note: the vast majority of social media act like silos. APIs play an important role in corporate business models, where industry controls the data it collects without reward, let alone user transparency. Negotiation of the specification of APIs to make data a common resource should be considered, for such an effort may align with the citizens’ interest….(More)”.

International Development Doesn’t Care About Patient Privacy

Curated on Posted on by Stefaan Verhulst

Yogesh Rajkotia at the Stanford Social Innovation Review: “In 2013, in southern Mozambique, foreign NGO workers searched for a man whom the local health facility reported as diagnosed with HIV. The workers aimed to verify that the health facility did indeed diagnose and treat him. When they could not find him, they asked the village chief for help. Together with an ever-growing crowd of onlookers, the chief led them to the man’s home. After hesitating and denying, he eventually admitted, in front of the crowd, that he had tested positive and received treatment. With his status made public, he now risked facing stigma, discrimination, and social marginalization. The incident undermined both his health and his ability to live a dignified life.

Similar privacy violations were documented in Burkina Faso in 2016, where community workers asked partners, in the presence of each other, to disclose what individual health services they had obtained.

Why was there such a disregard for the privacy and dignity of these citizens?

As it turns out, unbeknownst to these Mozambican and Burkinabé patients, their local health centers were participating in performance-based financing (PBF) programs financed by foreign assistance agencies. Implemented in more than 35 countries, PBF programs offer health workers financial bonuses for delivering priority health interventions. To ensure that providers do not cheat the system, PBF programs often send verifiers to visit patients’ homes to confirm that they have received specific health services. These verifiers are frequently community members (the World Bank callously notes in its “Performance-Based Financing Toolkit” that even “a local soccer club” can play this role), and this practice, known as “patient tracing,” is common among PBF programs. In World Bank-funded PBF programs alone, 19 out of the 25 PBF programs implement patient tracing. Yet the World Bank’s toolkit never mentions patient privacy or confidentiality. In patient tracing, patients’ rights and dignity are secondary to donor objectives.

Patient tracing within PBF programs is just one example of a bigger problem: Privacy violations are pervasive in global health. Some researchers and policymakers have raised privacy concerns about tuberculosis (TB), human immunodeficiency virus (HIV), family planning, post-abortion care, and disease surveillance programs. A study conducted by the Asia-Pacific Network of People Living with HIV/AIDS found that 34 percent of people living with HIV in India, Indonesia, Philippines, and Thailand reported that health workers breached confidentiality. In many programs, sensitive information about people’s sexual and reproductive health, disease status, and other intimate health details are often collected to improve health system effectiveness and efficiency. Usually, households have no way to opt out, nor any control over how heath care programs use, store, and disseminate this data. At the same time, most programs do not have systems to enforce health workers’ non-disclosure of private information.

In societies with strong stigma around certain health topics—especially sexual and reproductive health—the disclosure of confidential patient information can destroy lives. In contexts where HIV is highly stigmatized, people living with HIV are 2.4 times more likely to delay seeking care until they are seriously ill. In addition to stigma’s harmful effects on people’s health, it can limit individuals’ economic opportunities, cause them to be socially marginalized, and erode their psychological wellbeing….(More)”.

Is Distributed Ledger Technology Built for Personal Data?

Curated on Posted on by Stefaan Verhulst

Paper by Henry Chang: “Some of the appealing characteristics of distributed ledger technology (DLT), which blockchain is a type of, include guaranteed integrity, disintermediation and distributed resilience. These characteristics give rise to the possible consequences of immutability, unclear ownership, universal accessibility and trans-border storage. These consequences have the potential to contravene data protection principles of Purpose Specification, Use Limitation, Data Quality, Individual Participation and Trans-Border Data Flow. This paper endeavors to clarify the various types of DLTs, how they work, why they exhibit the depicted characteristics and the consequences. Using the universal privacy principles developed by the Organisation of Economic Cooperation and Development (OECD), this paper then describes how each of the consequence causes concerns for privacy protection and how attempts are being made to address them in the design and implementation of various applications of blockchain and DLT, and indicates where further research and best-practice developments lie….(More)”.

Making Better Use of Health Care Data

Curated on Posted on by Stefaan Verhulst

Benson S. Hsu, MD and Emily Griese in Harvard Business Review: “At Sanford Health, a $4.5 billion rural integrated health care system, we deliver care to over 2.5 million people in 300 communities across 250,000 square miles. In the process, we collect and store vast quantities of patient data – everything from admission, diagnostic, treatment and discharge data to online interactions between patients and providers, as well as data on providers themselves. All this data clearly represents a rich resource with the potential to improve care, but until recently was underutilized. The question was, how best to leverage it.

While we have a mature data infrastructure including a centralized data and analytics team, a standalone virtual data warehouse linking all data silos, and strict enterprise-wide data governance, we reasoned that the best way forward would be to collaborate with other institutions that had additional and complementary data capabilities and expertise.

We reached out to potential academic partners who were leading the way in data science, from university departments of math, science, and computer informatics to business and medical schools and invited them to collaborate with us on projects that could improve health care quality and lower costs. In exchange, Sanford created contracts that gave these partners access to data whose use had previously been constrained by concerns about data privacy and competitive-use agreements. With this access, academic partners are advancing their own research while providing real-world insights into care delivery.

The resulting Sanford Data Collaborative, now in its second year, has attracted regional and national partners and is already beginning to deliver data-driven innovations that are improving care delivery, patient engagement, and care access. Here we describe three that hold particular promise.

  • Developing Prescriptive Algorithms…
  • Augmenting Patient Engagement…
  • Improving Access to Care…(More)”.

Infection forecasts powered by big data

Curated on Posted on by Stefaan Verhulst

Michael Eisenstein at Nature: “…The good news is that the present era of widespread access to the Internet and digital health has created a rich reservoir of valuable data for researchers to dive into….By harvesting and combining these streams of big data with conventional ways of monitoring infectious diseases, the public-health community could gain fresh powers to catch and curb emerging outbreaks before they rage out of control.

Going viral

Data scientists at Google were the first to make a major splash using data gathered online to track infectious diseases. The Google Flu Trends algorithm, launched in November 2008, combed through hundreds of billions of users’ queries on the popular search engine to look for small increases in flu-related terms such as symptoms or vaccine availability. Initial data suggested that Google Flu Trends could accurately map the incidence of flu with a lag of roughly one day. “It was a very exciting use of these data for the purpose of public health,” says Brownstein. “It really did start a whole revolution and new field of work in query data.”

Unfortunately, Google Flu Trends faltered when it mattered the most, completely missing the onset in April 2009 of the H1N1 pandemic. The algorithm also ran into trouble later on in the pandemic. It had been trained against seasonal fluctuations of flu, says Viboud, but people’s behaviour changed in the wake of panic fuelled by media reports — and that threw off Google’s data. …

Nevertheless, its work with Internet usage data was inspirational for infectious-disease researchers. A subsequent study from a team led by Cecilia Marques-Toledo at the Federal University of Minas Gerais in Belo Horizonte, Brazil, used Twitter to get high-resolution data on the spread of dengue fever in the country. The researchers could quickly map new cases to specific cities and even predict where the disease might spread to next (C. A. Marques-Toledo et al. PLoS Negl. Trop. Dis. 11, e0005729; 2017). Similarly, Brownstein and his colleagues were able to use search data from Google and Twitter to project the spread of Zika virus in Latin America several weeks before formal outbreak declarations were made by public-health officials. Both Internet services are used widely, which makes them data-rich resources. But they are also proprietary systems for which access to data is controlled by a third party; for that reason, Generous and his colleagues have opted instead to make use of search data from Wikipedia, which is open source. “You can get the access logs, and how many people are viewing articles, which serves as a pretty good proxy for search interest,” he says.

However, the problems that sank Google Flu Trends still exist….Additionally, online activity differs for infectious conditions with a social stigma such as syphilis or AIDS, because people who are or might be affected are more likely to be concerned about privacy. Appropriate search-term selection is essential: Generous notes that initial attempts to track flu on Twitter were confounded by irrelevant tweets about ‘Bieber fever’ — a decidedly non-fatal condition affecting fans of Canadian pop star Justin Bieber.

Alternatively, researchers can go straight to the source — by using smartphone apps to ask people directly about their health. Brownstein’s team has partnered with the Skoll Global Threats Fund to develop an app called Flu Near You, through which users can voluntarily report symptoms of infection and other information. “You get more detailed demographics about age and gender and vaccination status — things that you can’t get from other sources,” says Brownstein. Ten European Union member states are involved in a similar surveillance programme known as Influenzanet, which has generally maintained 30,000–40,000 active users for seven consecutive flu seasons. These voluntary reporting systems are particularly useful for diseases such as flu, for which many people do not bother going to the doctor — although it can be hard to persuade people to participate for no immediate benefit, says Brownstein. “But we still get a good signal from the people that are willing to be a part of this.”…(More)”.

No One Owns Data

Curated on Posted on by Stefaan Verhulst

Paper by Lothar Determann: “Businesses, policy makers, and scholars are calling for property rights in data. They currently focus particularly on the vast amounts of data generated by connected cars, industrial machines, artificial intelligence, toys and other devices on the Internet of Things (IoT). This data is personal to numerous parties who are associated with a connected device, for example, the driver of a connected car, its owner and passengers, as well as other traffic participants. Manufacturers, dealers, independent providers of auto parts and services, insurance companies, law enforcement agencies and many others are also interested in this data. Various parties are actively staking their claims to data on the Internet of Things, as they are mining data, the fuel of the digital economy.

Stakeholders in digital markets often frame claims, negotiations and controversies regarding data access as one of ownership. Businesses regularly assert and demand that they own data. Individual data subjects also assume that they own data about themselves. Policy makers and scholars focus on how to redistribute ownership rights to data. Yet, upon closer review, it is very questionable whether data is—or should be—subject to any property rights. This article unambiguously answers the question in the negative, both with respect to existing law and future lawmaking, in the United States as in the European Union, jurisdictions with notably divergent attitudes to privacy, property and individual freedoms….

The article begins with a brief review of the current landscape of the Internet of Things notes explosive growth of data pools generated by connected devices, artificial intelligence, big data analytics tools and other information technologies. Part 1 lays the foundation for examining concrete current legal and policy challenges in the remainder of the article. Part 2 supplies conceptual differentiation and definitions with respect to “data” and “information” as the subject of rights and interests. Distinctions and definitional clarity serve as the basis for examining the purposes and reach of existing property laws in Part 3, including real property, personal property and intellectual property laws. Part 4 analyzes the effect of data-related laws that do not grant property rights. Part 5 examines how the interests of the various stakeholders are protected or impaired by the current framework of data-related laws to identify potential gaps that could warrant additional property rights. Part 6 examines policy considerations for and against property rights in data. Part 7 concludes that no one owns data and no one should own data….(More)”.