Selected Readings on Data Governance

Curated on Posted on by Jos Berens

Jos Berens (Centre for Innovation, Leiden University) and Stefaan G. Verhulst (GovLab)

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 governance was originally published in 2015.

Context
The field of Data Collaboratives is premised on the idea that sharing and opening-up private sector datasets has great – and yet untapped – potential for promoting social good. At the same time, the potential of data collaboratives depends on the level of societal trust in the exchange, analysis and use of the data exchanged. Strong data governance frameworks are essential to ensure responsible data use. Without such governance regimes, the emergent data ecosystem will be hampered and the (perceived) risks will dominate the (perceived) benefits. Further, without adopting a human-centered approach to the design of data governance frameworks, including iterative prototyping and careful consideration of the experience, the responses may fail to be flexible and targeted to real needs.

Selected Readings List (in alphabetical order)

Annotated Selected Readings List (in alphabetical order)

Better Place Lab, “Privacy, Transparency and Trust.” Mozilla, 2015. Available from: http://www.betterplace-lab.org/privacy-report.

  • This report looks specifically at the risks involved in the social sector having access to datasets, and the main risks development organizations should focus on to develop a responsible data use practice.
  • Focusing on five specific countries (Brazil, China, Germany, India and Indonesia), the report displays specific country profiles, followed by a comparative analysis centering around the topics of privacy, transparency, online behavior and trust.
  • Some of the key findings mentioned are:
    • A general concern on the importance of privacy, with cultural differences influencing conception of what privacy is.
    • Cultural differences determining how transparency is perceived, and how much value is attached to achieving it.
    • To build trust, individuals need to feel a personal connection or get a personal recommendation – it is hard to build trust regarding automated processes.

Montjoye, Yves Alexandre de; Kendall, Jake and; Kerry, Cameron F. “Enabling Humanitarian Use of Mobile Phone Data.” The Brookings Institution, 2015. Available from: http://www.brookings.edu/research/papers/2014/11/12-enabling-humanitarian-use-mobile-phone-data.

  • Focussing in particular on mobile phone data, this paper explores ways of mitigating privacy harms involved in using call detail records for social good.
  • Key takeaways are the following recommendations for using data for social good:
    • Engaging companies, NGOs, researchers, privacy experts, and governments to agree on a set of best practices for new privacy-conscientious metadata sharing models.
    • Accepting that no framework for maximizing data for the public good will offer perfect protection for privacy, but there must be a balanced application of privacy concerns against the potential for social good.
    • Establishing systems and processes for recognizing trusted third-parties and systems to manage datasets, enable detailed audits, and control the use of data so as to combat the potential for data abuse and re-identification of anonymous data.
    • Simplifying the process among developing governments in regards to the collection and use of mobile phone metadata data for research and public good purposes.

Centre for Democracy and Technology, “Health Big Data in the Commercial Context.” Centre for Democracy and Technology, 2015. Available from: https://cdt.org/insight/health-big-data-in-the-commercial-context/.

  • Focusing particularly on the privacy issues related to using data generated by individuals, this paper explores the overlap in privacy questions this field has with other data uses.
  • The authors note that although the Health Insurance Portability and Accountability Act (HIPAA) has proven a successful approach in ensuring accountability for health data, most of these standards do not apply to developers of the new technologies used to collect these new data sets.
  • For non-HIPAA covered, customer facing technologies, the paper bases an alternative framework for consideration of privacy issues. The framework is based on the Fair Information Practice Principles, and three rounds of stakeholder consultations.

Center for Information Policy Leadership, “A Risk-based Approach to Privacy: Improving Effectiveness in Practice.” Centre for Information Policy Leadership, Hunton & Williams LLP, 2015. Available from: https://www.informationpolicycentre.com/uploads/5/7/1/0/57104281/white_paper_1-a_risk_based_approach_to_privacy_improving_effectiveness_in_practice.pdf.

  • This white paper is part of a project aiming to explain what is often referred to as a new, risk-based approach to privacy, and the development of a privacy risk framework and methodology.
  • With the pace of technological progress often outstripping the capabilities of privacy officers to keep up, this method aims to offer the ability to approach privacy matters in a structured way, assessing privacy implications from the perspective of possible negative impact on individuals.
  • With the intended outcomes of the project being “materials to help policy-makers and legislators to identify desired outcomes and shape rules for the future which are more effective and less burdensome”, insights from this paper might also feed into the development of innovative governance mechanisms aimed specifically at preventing individual harm.

Centre for Information Policy Leadership, “Data Governance for the Evolving Digital Market Place”, Centre for Information Policy Leadership, Hunton & Williams LLP, 2011. Available from: http://www.huntonfiles.com/files/webupload/CIPL_Centre_Accountability_Data_Governance_Paper_2011.pdf.

  • This paper argues that as a result of the proliferation of large scale data analytics, new models governing data inferred from society will shift responsibility to the side of organizations deriving and creating value from that data.
  • It is noted that, with the reality of the challenge corporations face of enabling agile and innovative data use “In exchange for increased corporate responsibility, accountability [and the governance models it mandates, ed.] allows for more flexible use of data.”
  • Proposed as a means to shift responsibility to the side of data-users, the accountability principle has been researched by a worldwide group of policymakers. Tailing the history of the accountability principle, the paper argues that it “(…) requires that companies implement programs that foster compliance with data protection principles, and be able to describe how those programs provide the required protections for individuals.”
  • The following essential elements of accountability are listed:
    • Organisation commitment to accountability and adoption of internal policies consistent with external criteria
    • Mechanisms to put privacy policies into effect, including tools, training and education
    • Systems for internal, ongoing oversight and assurance reviews and external verification
    • Transparency and mechanisms for individual participation
    • Means of remediation and external enforcement

Crawford, Kate; Schulz, Jason. “Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harm.” NYU School of Law, 2014. Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2325784&download=yes.

  • Considering the privacy implications of large-scale analysis of numerous data sources, this paper proposes the implementation of a ‘procedural data due process’ mechanism to arm data subjects against potential privacy intrusions.
  • The authors acknowledge that some privacy protection structures already know similar mechanisms. However, due to the “inherent analytical assumptions and methodological biases” of big data systems, the authors argue for a more rigorous framework.

Letouze, Emmanuel, and; Vinck, Patrick. “The Ethics and Politics of Call Data Analytics”, DataPop Alliance, 2015. Available from: http://static1.squarespace.com/static/531a2b4be4b009ca7e474c05/t/54b97f82e4b0ff9569874fe9/1421442946517/WhitePaperCDRsEthicFrameworkDec10-2014Draft-2.pdf.

  • Focusing on the use of Call Detail Records (CDRs) for social good in development contexts, this whitepaper explores both the potential of these datasets – in part by detailing recent successful efforts in the space – and political and ethical constraints to their use.
  • Drawing from the Menlo Report Ethical Principles Guiding ICT Research, the paper explores how these principles might be unpacked to inform an ethics framework for the analysis of CDRs.

Data for Development External Ethics Panel, “Report of the External Ethics Review Panel.” Orange, 2015. Available from: http://www.d4d.orange.com/fr/content/download/43823/426571/version/2/file/D4D_Challenge_DEEP_Report_IBE.pdf.

  • This report presents the findings of the external expert panel overseeing the Orange Data for Development Challenge.
  • Several types of issues faced by the panel are described, along with the various ways in which the panel dealt with those issues.

Federal Trade Commission Staff Report, “Mobile Privacy Disclosures: Building Trust Through Transparency.” Federal Trade Commission, 2013. Available from: www.ftc.gov/os/2013/02/130201mobileprivacyreport.pdf.

  • This report looks at ways to address privacy concerns regarding mobile phone data use. Specific advise is provided for the following actors:
    • Platforms, or operating systems providers
    • App developers
    • Advertising networks and other third parties
    • App developer trade associations, along with academics, usability experts and privacy researchers

Mirani, Leo. “How to use mobile phone data for good without invading anyone’s privacy.” Quartz, 2015. Available from: http://qz.com/398257/how-to-use-mobile-phone-data-for-good-without-invading-anyones-privacy/.

  • This paper considers the privacy implications of using call detail records for social good, and ways to mitigate risks of privacy intrusion.
  • Taking example of the Orange D4D challenge and the anonymization strategy that was employed there, the paper describes how classic ‘anonymization’ is often not enough. The paper then lists further measures that can be taken to ensure adequate privacy protection.

Bernholz, Lucy. “Several Examples of Digital Ethics and Proposed Practices” Stanford Ethics of Data conference, 2014, Available from: http://www.scribd.com/doc/237527226/Several-Examples-of-Digital-Ethics-and-Proposed-Practices.

  • This list of readings prepared for Stanford’s Ethics of Data conference lists some of the leading available literature regarding ethical data use.

Abrams, Martin. “A Unified Ethical Frame for Big Data Analysis.” The Information Accountability Foundation, 2014. Available from: http://www.privacyconference2014.org/media/17388/Plenary5-Martin-Abrams-Ethics-Fundamental-Rights-and-BigData.pdf.

  • Going beyond privacy, this paper discusses the following elements as central to developing a broad framework for data analysis:
    • Beneficial
    • Progressive
    • Sustainable
    • Respectful
    • Fair

Lane, Julia; Stodden, Victoria; Bender, Stefan, and; Nissenbaum, Helen, “Privacy, Big Data and the Public Good”, Cambridge University Press, 2014. Available from: http://www.dataprivacybook.org.

  • This book treats the privacy issues surrounding the use of big data for promoting the public good.
  • The questions being asked include the following:
    • What are the ethical and legal requirements for scientists and government officials seeking to serve the public good without harming individual citizens?
    • What are the rules of engagement?
    • What are the best ways to provide access while protecting confidentiality?
    • Are there reasonable mechanisms to compensate citizens for privacy loss?

Richards, Neil M, and; King, Jonathan H. “Big Data Ethics”. Wake Forest Law Review, 2014. Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2384174.

  • This paper describes the growing impact of big data analytics on society, and argues that because of this impact, a set of ethical principles to guide data use is called for.
  • The four proposed themes are: privacy, confidentiality, transparency and identity.
  • Finally, the paper discusses how big data can be integrated into society, going into multiple facets of this integration, including the law, roles of institutions and ethical principles.

OECD, “OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data”. Available from: http://www.oecd.org/sti/ieconomy/oecdguidelinesontheprotectionofprivacyandtransborderflowsofpersonaldata.htm.

  • A globally used set of principles to inform thought about handling personal data, the OECD privacy guidelines serve as one the leading standards for informing privacy policies and data governance structures.
  • The basic principles of national application are the following:
    • Collection Limitation Principle
    • Data Quality Principle
    • Purpose Specification Principle
    • Use Limitation Principle
    • Security Safeguards Principle
    • Openness Principle
    • Individual Participation Principle
    • Accountability Principle

The White House Big Data and Privacy Working Group, “Big Data: Seizing Opportunities, Preserving Values”, White House, 2015. Available from: https://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_5.1.14_final_print.pdf.

  • Documenting the findings of the White House big data and privacy working group, this report lists i.a. the following key recommendations regarding data governance:
    • Bringing greater transparency to the data services industry
    • Stimulating international conversation on big data, with multiple stakeholders
    • With regard to educational data: ensuring data is used for the purpose it is collected for
    • Paying attention to the potential for big data to facilitate discrimination, and expanding technical understanding to stop discrimination

William Hoffman, “Pathways for Progress” World Economic Forum, 2015. Available from: http://www3.weforum.org/docs/WEFUSA_DataDrivenDevelopment_Report2015.pdf.

  • This paper treats i.a. the lack of well-defined and balanced governance mechanisms as one of the key obstacles preventing particularly corporate sector data from being shared in a controlled space.
  • An approach that balances the benefits against the risks of large scale data usage in a development context, building trust among all stake holders in the data ecosystem, is viewed as key.
  • Furthermore, this whitepaper notes that new governance models are required not just by the growing amount of data and analytical capacity, and more refined methods for analysis. The current “super-structure” of information flows between institutions is also seen as one of the key reasons to develop alternatives to the current – outdated – approaches to data governance.

Chicago uses new technology to solve this very old urban problem

Curated on Posted on by Stefaan Verhulst

Mehboob Jeelani at Fortune: “Chicago has spent 12 years collecting data on resident complaints. Now the city is harnessing that data to control the rat population, stopping infestations before residents spot rats in the first place.

For the past three years, Chicago police have been analyzing 911 calls to better predict crime patterns across the city and, in one case, actually forecasted a shootout minutes before it occurred.

Now, the city government is turning its big data weapons on the city’s rat population.

The city has 12 years of data on the resident complaints, ranging from calls about rodent sitting to graffiti. Those clusters of data lead the engineers to where the rats can potentially breed. The report is shared with the city’s sanitation team, which later cleans up the rat-infested areas.

“We discovered really interesting relationship that led to developing an algorithm about rodent prediction,” says Brenna Berman, Chicago’s chief information officer. “It involved 31 variables related to calls about overflowing trash bins and food poisoning in restaurants.”

The results, Berman says, are 20% more efficient versus the old responsive model.

Governing cities in the 21st century is a difficult task. It needs a political and economic support. In America, it was only in the early 1990s—when young adults started moving from the suburbs back to the cities—that the academic and policy consensus shifted back toward urban centers. Since then, cities are facing an influx of new residents, overwhelming the service providing agencies. To meet that demand amid the recent budget sequestration, cities like New York, San Francisco, Philadelphia, and Chicago are constantly elevating the art of governance through innovative policies.

Due to this new model, in Chicago, you might not even spot a rat. The city’s Department of Innovation and Technology analyzes big chunks of data to an extent where the likelihood of a rodent infestation is thwarted seven days ahead of resident rat-sightings…(More)”

Apple Has Plans for Your DNA

Curated on Posted on by Stefaan Verhulst

Antonio Regalado at MIT Technology Review: “…Apple is collaborating with U.S. researchers to launch apps that would offer some iPhone owners the chance to get their DNA tested, many of them for the first time, according to people familiar with the plans.

The apps are based on ResearchKit, a software platform Apple introduced in March that helps hospitals or scientists run medical studies on iPhones by collecting data from the devices’ sensors or through surveys.

The first five ResearchKit apps, including one called mPower that tracks symptoms of Parkinson’s disease, quickly recruited thousands of participants in a few days, demonstrating the reach of Apple’s platform.

“Apple launched ResearchKit and got a fantastic response. The obvious next thing is to collect DNA,” says Gholson Lyon, a geneticist at Cold Spring Harbor Laboratory, who isn’t involved with the studies.

Nudging iPhone owners to submit DNA samples to researchers would thrust Apple’s devices into the center of a widening battle for genetic information. Universities, large technology companies like Google (see “Google Wants to Store Your Genome”), direct-to-consumer labs, and even the U.S. government (see “U.S. to Develop DNA Study of One Million People”) are all trying to amass mega-databases of gene information to uncover clues about the causes of disease (see “Internet of DNA”).

In two initial studies planned, Apple isn’t going to directly collect or test DNA itself. That will be done by academic partners. The data would be maintained by scientists in a computing cloud, but certain findings could appear directly on consumers’ iPhones as well. Eventually, it’s even possible consumers might swipe to share “my genes” as easily as they do their location….(More)”

Five Headlines from a Big Month for the Data Revolution

Curated on Posted on by Stefaan Verhulst

Sarah T. Lucas at Post2015.org: “If the history of the data revolution were written today, it would include three major dates. May 2013, when theHigh Level Panel on the Post-2015 Development Agenda first coined the phrase “data revolution.” November 2014, when the UN Secretary-General’s Independent Expert Advisory Group (IEAG) set a vision for it. And April 2015, when five headliner stories pushed the data revolution from great idea to a concrete roadmap for action.

The April 2015 Data Revolution Headlines

1. The African Data Consensus puts Africa in the lead on bringing the data revolution to the regional level. TheAfrica Data Consensus (ADC) envisions “a profound shift in the way that data is harnessed to impact on development decision-making, with a particular emphasis on building a culture of usage.” The ADC finds consensus across 15 “data communities”—ranging from open data to official statistics to geospatial data, and is endorsed by Africa’s ministers of finance. The ADC gets top billing in my book, as the first contribution that truly reflects a large diversity of voices and creates a political hook for action. (Stay tuned for a blog from my colleague Rachel Quint on the ADC).

2. The Sustainable Development Solutions Network (SDSN) gets our minds (and wallets) around the data needed to measure the SDGs. The SDSN Needs Assessment for SDG Monitoring and Statistical Capacity Development maps the investments needed to improve official statistics. My favorite parts are the clear typology of data (see pg. 12), and that the authors are very open about the methods, assumptions, and leaps of faith they had to take in the costing exercise. They also start an important discussion about how advances in information and communications technology, satellite imagery, and other new technologies have the potential to expand coverage, increase analytic capacity, and reduce the cost of data systems.

3. The Overseas Development Institute (ODI) calls on us to find the “missing millions.” ODI’s The Data Revolution: Finding the Missing Millions presents the stark reality of data gaps and what they mean for understanding and addressing development challenges. The authors highlight that even that most fundamental of measures—of poverty levels—could be understated by as much as a quarter. And that’s just the beginning. The report also pushes us to think beyond the costs of data, and focus on how much good data can save. With examples of data lowering the cost of doing government business, the authors remind us to think about data as an investment with real economic and social returns.

4. Paris21 offers a roadmap for putting national statistic offices (NSOs) at the heart of the data revolution.Paris21’s Roadmap for a Country-Led Data Revolution does not mince words. It calls on the data revolution to “turn a vicious cycle of [NSO] underperformance and inadequate resources into a virtuous one where increased demand leads to improved performance and an increase in resources and capacity.” It makes the case for why NSOs are central and need more support, while also pushing them to modernize, innovate, and open up. The roadmap gets my vote for best design. This ain’t your grandfather’s statistics report!

5. The Cartagena Data Festival features real-live data heroes and fosters new partnerships. The Festival featured data innovators (such as terra-i using satellite data to track deforestation), NSOs on the leading edge of modernization and reform (such as Colombia and the Philippines), traditional actors using old data in new ways (such as the Inter-American Development Bank’s fantastic energy database), groups focused on citizen-generated data (such as The Data Shift and UN My World), private firms working with big data for social good (such asTelefónica), and many others—all reminding us that the data revolution is well underway and will not be stopped. Most importantly, it brought these actors together in one place. You could see the sparks flying as folks learned from each other and hatched plans together. The Festival gets my vote for best conference of a lifetime, with the perfect blend of substantive sessions, intense debate, learning, inspiration, new connections, and a lot of fun. (Stay tuned for a post from my colleague Kristen Stelljes and me for more on Cartagena).

This month full of headlines leaves no room for doubt—momentum is building fast on the data revolution. And just in time.

With the Financing for Development (FFD) conference in Addis Ababa in July, the agreement of Sustainable Development Goals in New York in September, and the Climate Summit in Paris in December, this is a big political year for global development. Data revolutionaries must seize this moment to push past vision, past roadmaps, to actual action and results…..(More)”

Data Fusion Heralds City Attractiveness Ranking

Curated on Posted on by Stefaan Verhulst

Emerging Technology From the arXiv: “The ability of any city to attract visitors is an important metric for town planners, businesses based on tourism, traffic planners, residents, and so on. And there are increasingly varied ways of measuring this thanks to the growing volumes of city-related data generated by with social media, and location-based data.

So it’s only natural that researchers would like to draw these data sets together to see what kind of insight they can get from this form of data fusion.

And so it has turned out thanks to the work of Stanislav Sobolevsky at MIT and a few buddies. These guys have fused three wildly different data sets related to the attractiveness of a city that allows them to rank these places and to understand why people visit them and what they do when they get there.

The work focuses exclusively on cities in Spain using data that is relatively straightforward to gather. The first data set consists of the number of credit and debit card transactions carried out by visitors to cities throughout Spain during 2011. This includes each card’s country of origin, which allows Sobolevsky and co to count only those transactions made by foreign visitors—a total of 17 million anonymized transactions from 8.6 million foreign visitors from 175 different countries.

The second data set consists of over 3.5 million photos and videos taken in Spain and posted to Flickr by people living in other countries. These pictures were taken between 2005 and 2014 by 16,000 visitors from 112 countries.

The last data set consists of around 700,000 geotagged tweets posted in Spain during 2012. These were posted by 16,000 foreign visitors from 112 countries.

Finally, the team defined a city’s attractiveness, at least for the purposes of this study, as the total number of pictures, tweets and card transactions that took place within it……

That’s interesting work that shows how the fusion of big data sets can provide insights into the way people use cities.   It has its limitations of course. The study does not address the reasons why people find cities attractive and what draws them there in the first place. For example, are they there for tourism, for business, or for some other reason. That would require more specialized data.

But it does provide a general picture of attractiveness that could be a start for more detailed analyses. As such, this work is just a small part of a new science of cities based on big data, but one that shows how much is becoming possible with just a little number crunching.

Ref: arxiv.org/abs/1504.06003 : Scaling of city attractiveness for foreign visitors through big data of human economic and social media activity”

How Not to Drown in Numbers

Curated on Posted on by Stefaan Verhulst

Seth Stephens-Davidowitz in the New York Times: “BIG data will save the world. How often have we heard that over the past couple of years? We’re pretty sure both of us have said something similar dozens of times in the past few months.

If you’re trying to build a self-driving car or detect whether a picture has a cat in it, big data is amazing. But here’s a secret: If you’re trying to make important decisions about your health, wealth or happiness, big data is not enough.

The problem is this: The things we can measure are never exactly what we care about. Just trying to get a single, easy-to-measure number higher and higher (or lower and lower) doesn’t actually help us make the right choice. For this reason, the key question isn’t “What did I measure?” but “What did I miss?”

…

So what can big data do to help us make big decisions? One of us, Alex, is a data scientist at Facebook. The other, Seth, is a former data scientist at Google. There is a special sauce necessary to making big data work: surveys and the judgment of humans — two seemingly old-fashioned approaches that we will call small data….(More)”

Urban Data Games: creating smart citizens for smart cities

Curated on Posted on by Stefaan Verhulst

Paper by Wolff, Annika; Kortuem, Gerd and Cavero, Jose: “A bottom-up approach to smart cities places citizens in an active role of contributing, analysing and interpreting data in pursuit of tackling local urban challenges and building a more sustainable future city. This vision can only be realised if citizens have sufficient data literacy skills and experience of large, complex, messy, ever expanding data sets. Schools typically focus on teaching data handling skills using small, personally collected data sets obtained through scientific experimentation, leading to a gap between what is being taught and what will be needed as big data and analytics become more prevalent. This paper proposes an approach to teaching data literacy in the context of urban innovation tasks, using an idea of Urban Data Games. These are supported by a set of training data and resources that will be used in school trials for exploring the problems people have when dealing with large data and trialling novel approaches for teaching data literacy….(More)”

A map for Big Data research in Digital Humanities

Curated on Posted on by Stefaan Verhulst

Article by Frederic Kaplan in Frontiers: “This article is an attempt to represent Big Data research in Digital Humanities as a structured research field. A division in three concentric areas of study is presented. Challenges in the first circle – focusing on the processing and interpretations of large cultural datasets – can be organized linearly following the data processing pipeline. Challenges in the second circle – concerning digital culture at large – can be structured around the different relations linking massive datasets, large communities, collective discourses, global actors and the software medium. Challenges in the third circle – dealing with the experience of big data – can be described within a continuous space of possible interfaces organized around three poles: immersion, abstraction and language. By identifying research challenges in all these domains, the article illustrates how this initial cartography could be helpful to organize the exploration of the various dimensions of Big Data Digital Humanities research….(More)”

How Data Mining could have prevented Tunisia’s Terror attack in Bardo Museum

Curated on Posted on by Stefaan Verhulst

Wassim Zoghlami at Medium: “…Data mining is the process of posing queries and extracting useful patterns or trends often previously unknown from large amounts of data using various techniques such as those from pattern recognition and machine learning. Latelely there has been a big interest on leveraging the use of data mining for counter-terrorism applications

Using the data on more than 50.000+ ISIS connected twitter accounts , I was able to establish an understanding of some factors determined how often ISIS attacks occur , what different types of terror strikes are used in which geopolitical situations, and many other criteria through graphs about the frequency of hashtags usages and the frequency of a particular group of the words used in the tweets.

A simple data mining project of some of the repetitive hashtags and sequences of words used typically by ISIS militants in their tweets yielded surprising results. The results show a rise of some keywords on the tweets that started from Marsh 15, three days before Bardo museum attacks.

Some of the common frequent keywords and hashtags that had a unusual peak since marsh 15 , three days before the attack :

#طواغيت تونس : Tyrants of Tunisia = a reference to the military

بشرى تونس : Good news for Tunisia.

قريبا تونس : Soon in Tunisia.

#إفريقية_للإعلام : The head of social media of Afriqiyah

#غزوة_تونس : The foray of Tunis…

Big Data and Data Mining should be used for national security intelligence

The Tunisian national security has to leverage big data to predict such attacks and to achieve objectives as the volume of digital data. Some of the challenges facing the Data mining techniques are that to carry out effective data mining and extract useful information for counterterrorism and national security, we need to gather all kinds of information about individuals. However, this information could be a threat to the individuals’ privacy and civil liberties…(More)”

Health Big Data in the Commercial Context

Curated on Posted on by Stefaan Verhulst

CDT Press Release: “This paper is the third in a series of three, each of which explores health big data in a different context. The first — on health big data in the government context — is available here, and the second — on health big data in the clinical context — is available here.

Consumers are increasingly using mobile phone apps and wearable devices to generate and share data on health and wellness. They are using personal health record tools to access and copy health records and move them to third party platforms. They are sharing health information on social networking sites. They leave digital health footprints when they conduct online searches for health information. The health data created, accessed, and shared by consumers using these and many other tools can range from detailed clinical information, such as downloads from an implantable device and details about medication regimens, to data about weight, caloric intake, and exercise logged with a smart phone app.

These developments offer a wealth of opportunities for health care and personal wellness. However, privacy questions arise due to the volume and sensitivity of health data generated by consumer-focused apps, devices, and platforms, including the potential analytics uses that can be made of such data.

Many of the privacy issues that face traditional health care entities in the big data era also apply to app developers, wearable device manufacturers, and other entities not part of the traditional health care ecosystem. These include questions of data minimization, retention, and secondary use. Notice and consent pose challenges, especially given the limits of presenting notices on mobile device screens, and the fact that consumer devices may be bought and used without consultation with a health care professional. Security is a critical issue as well.

However, the privacy and security provisions of the Heath Insurance Portability and Accountability Act (HIPAA) do not apply to most app developers, device manufacturers or others in the consumer health space. This has benefits to innovation, as innovators would otherwise have to struggle with the complicated HIPAA rules. However, the current vacuum also leaves innovators without clear guidance on how to appropriately and effectively protect consumers’ health data. Given the promise of health apps, consumer devices, and consumer-facing services, and given the sensitivity of the data that they collect and share, it is important to provide such guidance….

As the source of privacy guidelines, we look to the framework provided by the Fair Information Practice Principles (FIPPs) and explore how it could be applied in an age of big data to patient-generated data. The FIPPs have influenced to varying degrees most modern data privacy regimes. While some have questioned the continued validity of the FIPPs in the current era of mass data collection and analysis, we consider here how the flexibility and rigor of the FIPPs provide an organizing framework for responsible data governance, promoting innovation, efficiency, and knowledge production while also protecting privacy. Rather than proposing an entirely new framework for big data, which could be years in the making at best, using the FIPPs would seem the best approach in promoting responsible big data practices. Applying the FIPPs could also help synchronize practices between the traditional health sector and emerging consumer products….(More)”