In 2014 Tim Berners-Lee, inventor of the World Wide Web, proposed an online ‘Magna Carta’ to protect the Internet, as a neutral system, from government and corporate manipulation. He was responding after revelations that British and US spy agencies were carrying out mass surveillance programmes; the Cambridge Analytica scandal makes his proposal as relevant as ever.
Luciano Floridi, professor of Philosophy and Ethics of Information at the Oxford Internet Institute, explains that grey power is not ordinary socio-political or military power. It is not the ability to directly influence others, but rather the power to influence those who influence power. To see grey power, you need only look at the hundreds of high-level instances of revolving-door staffing patterns between Google and European governmentsand the U.S. Department of State.
And then there is ‘surveillance capitalism’. Shoshana Zuboff, Professor Emerita at Harvard Business School, proposes that surveillance capitalism is ‘a new logic of accumulation’. The incredible evolution of computer processing power, complex algorithms and leaps in data storage capabilities combine to make surveillance capitalism possible. It is the process of accumulation by dispossession of the data that people produce.
The respected security technologist Bruce Schneier recently applied the insights of surveillance capitalism to the Cambridge Analytica/Facebook crisis.
For Schneier, ‘regulation is the only answer.’ He cites the EU’s General Data Protection Regulation coming into effect next month, which stipulates that users must consent to what personal data can be saved and how it is used.
Apps like WhatsApp, Facebook, Snapchat, Instagram, Twitter, LinkedIn, Viber
Felix Krause described in 2017 that when a user grants an app access to their camera and microphone, the app could do the following:
Access both the front and the back camera.
Record you at any time the app is in the foreground.
Take pictures and videos without telling you.
Upload the pictures and videos without telling you.
Upload the pictures/videos it takes immediately.
Run real-time face recognition to detect facial features or expressions.
Livestream the camera on to the internet.
Detect if the user is on their phone alone, or watching together with a second person.
Upload random frames of the video stream to your web service and run a proper face recognition software which can find existing photos of you on the internet and create a 3D model based on your face.
For instance, here’s a Find my Phone application which a documentary maker installed on a phone, then let someone steal it. After the person stole it, the original owner spied on every moment of the thief’s life through the phone’s camera and microphone.
Edward Snowden revealed an NSA program called Optic Nerves. The operation was a bulk surveillance program under which they captured webcam images every five minutes from Yahoo users’ video chats and then stored them for future use. It is estimated that between 3% and 11% of the images captured contained “undesirable nudity”.
Hackers can also gain access to your device with extraordinary ease via apps, PDF files, multimedia messages and even emojis.
An application called Metasploit on the ethical hacking platform Kali uses an Adobe Reader 9 (which over 60% of users still use) exploit to open a listener (rootkit) on the user’s computer. You alter the PDF with the program, send the user the malicious file, they open it, and hey presto – you have total control over their device remotely.
Once a user opens this PDF file, the hacker can then:
Install whatever software/app they like on the user’s device.
Use a keylogger to grab all of their passwords.
Steal all documents from the device.
Take pictures and stream videos from their camera.
Capture past or live audio from the microphone.
Upload incriminating images/documents to their PC, and notify the police.
And, if it’s not enough that your phone is tracking you – surveillance cameras in shops and streets are tracking you, too
You might even be on this website, InSeCam, which allows ordinary people online to watch surveillance cameras free of charge. It even allows you to search cameras by location, city, time zone, device manufacturer, and specify whether you want to see a kitchen, bar, restaurant or bedroom.
until recently, broadcasting and publishing were difficult and expensive affairs, their infrastructures riddled with bottlenecks and concentrated in a few hands.
When protests broke out in Ferguson, Missouri, in August 2014, a single livestreamer named Mustafa Hussein reportedly garnered an audience comparable in size to CNN’s for a short while. If a Bosnian Croat war criminal drinks poison in a courtroom, all of Twitter knows about it in minutes.
In today’s networked environment, when anyone can broadcast live or post their thoughts to a social network, it would seem that censorship ought to be impossible. This should be the golden age of free speech.
And sure, it is a golden age of free speech—if you can believe your lying eyes. Is that footage you’re watching real? Was it really filmed where and when it says it was? Is it being shared by alt-right trolls or a swarm of Russian bots? My note: see the ability to create fake audio and video footage: http://blog.stcloudstate.edu/ims/2017/07/15/fake-news-and-video/
HERE’S HOW THIS golden age of speech actually works: In the 21st century, the capacity to spread ideas and reach an audience is no longer limited by access to expensive, centralized broadcasting infrastructure. It’s limited instead by one’s ability to garner and distribute attention. And right now, the flow of the world’s attention is structured, to a vast and overwhelming degree, by just a few digital platforms: Facebook, Google (which owns YouTube), and, to a lesser extent, Twitter.
at their core, their business is mundane: They’re ad brokers
They use massive surveillance of our behavior, online and off, to generate increasingly accurate, automated predictions of what advertisements we are most susceptible to and what content will keep us clicking, tapping, and scrolling down a bottomless feed.
in reality, posts are targeted and delivered privately, screen by screen by screen. Today’s phantom public sphere has been fragmented and submerged into billions of individual capillaries. Yes, mass discourse has become far easier for everyone to participate in—but it has simultaneously become a set of private conversations happening behind your back. Behind everyone’s backs.
It’s important to realize that, in using these dark posts, the Trump campaign wasn’t deviantly weaponizing an innocent tool. It was simply using Facebook exactly as it was designed to be used. The campaign did it cheaply, with Facebook staffers assisting right there in the office, as the tech company does for most large advertisers and political campaigns.
China Central Television (formerly Beijing Television), commonly abbreviated as CCTV, is the predominant statetelevision broadcaster in the People’s Republic of China. CCTV has a network of 50 channels broadcasting different programmes and is accessible to more than one billion viewers. As of present, there are 50 television channels, and the broadcaster provides programming in six different languages. Most of its programmes are a mixture of news, documentary, social education, comedy, entertainment, and drama, the majority of which consists of Chinese soap operas and entertainment.
Media literacy. Differentiated instruction. Media literacy guide.
Fake news as part of media literacy. Visual literacy as part of media literacy. Media literacy as part of digital citizenship.
Web design / web development
the roles of HTML5, CSS, Java Script, PHP, Bootstrap, JQuery, React and other scripting languages and libraries. Heat maps and other usability issues; website content strategy. THE MODEL-VIEW-CONTROLLER (MVC) design pattern
Social media for institutional use. Digital Curation. Social Media algorithms. Etiquette Ethics. Mastodon
I hosted a LITA webinar in the fall of 2016 (four weeks); I can accommodate any information from that webinar for the use of the IM students
OER and instructional designer’s assistance to book creators.
I can cover both the “library part” (“free” OER, copyright issues etc) and the support / creative part of an OER book / textbook
“Big Data.” Data visualization. Large scale visualization. Text encoding. Analytics, Data mining. Unizin. Python, R in academia.
I can introduce the students to the large idea of Big Data and its importance in lieu of the upcoming IoT, but also departmentalize its importance for academia, business, etc. From infographics to heavy duty visualization (Primo X-Services API. JSON, Flask).
NetNeutrality, Digital Darwinism, Internet economy and the role of your professional in such environment
I can introduce students to the issues, if not familiar and / or lead a discussion on a rather controversial topic
Digital assessment. Digital Assessment literacy.
I can introduce students to tools, how to evaluate and select tools and their pedagogical implications
a hands-on exercise on working with Wikipedia. After the session, students will be able to create Wikipedia entries thus knowing intimately the process of Wikipedia and its information.
Effective presentations. Tools, methods, concepts and theories (cognitive load). Presentations in the era of VR, AR and mixed reality. Unity.
I can facilitate a discussion among experts (your students) on selection of tools and their didactically sound use to convey information. I can supplement the discussion with my own findings and conclusions.
eConferencing. Tools and methods
I can facilitate a discussion among your students on selection of tools and comparison. Discussion about the their future and their place in an increasing online learning environment
Digital Storytelling. Immersive Storytelling. The Moth. Twine. Transmedia Storytelling
I am teaching a LIB 490/590 Digital Storytelling class. I can adapt any information from that class to the use of IM students
VR, AR, Mixed Reality.
besides Mark Gill, I can facilitate a discussion, which goes beyond hardware and brands, but expand on the implications for academia and corporate education / world
Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.
Learning to Harness Big Data in an Academic Library
Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions. Introduction to the problem. Limitations
The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.
Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.
In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).
The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).
Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).
De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).
The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics. The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).
Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).
Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?
Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.
Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success. Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).
Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.
Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).
The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.
Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.
The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data. Send survey URL to (academic?) libraries around the world.
Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.
The study will include the use of closed-ended survey response questions and open-ended questions. The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.
Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17). Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).
Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.
Complete literature review and identify areas of interest – two months
Prepare and test instrument (survey) – month
IRB and other details – month
Generate a list of potential libraries to distribute survey – month
Contact libraries. Follow up and contact again, if necessary (low turnaround) – month
Collect, analyze data – two months
Write out data findings – month
Complete manuscript – month
Proofreading and other details – month
Significance of the work
While it has been widely acknowledged that Big Data (and its handling) is changing higher education (http://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (http://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).
This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.
Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.
Adams Becker, S., Cummins M, Davis, A., Freeman, A., Giesinger Hall, C., Ananthanarayanan, V., … Wolfson, N. (2017). NMC Horizon Report: 2017 Library Edition.
Andrejevic, M., & Gates, K. (2014). Big Data Surveillance: Introduction. Surveillance & Society, 12(2), 185–196.
Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125
Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD Skills: New Analysis Practices for Big Data. Proc. VLDB Endow., 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576
Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., … Chuang, I. (2014). Privacy, Anonymity, and Big Data in the Social Sciences. Commun. ACM, 57(9), 56–63. https://doi.org/10.1145/2643132
De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644(1), 97–104. https://doi.org/10.1063/1.4907823
Emanuel, J. (2013). Usability testing in libraries: methods, limitations, and implications. OCLC Systems & Services: International Digital Library Perspectives, 29(4), 204–217. https://doi.org/10.1108/OCLC-02-2013-0009
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47(Supplement C), 98–115. https://doi.org/10.1016/j.is.2014.07.006
Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347. https://doi.org/10.1016/j.ins.2014.01.015
Sugimoto, C. R., Ding, Y., & Thelwall, M. (2012). Library and information science in the big data era: Funding, projects, and future [a panel proposal]. Proceedings of the American Society for Information Science and Technology, 49(1), 1–3. https://doi.org/10.1002/meet.14504901187
Tene, O., & Polonetsky, J. (2012). Big Data for All: Privacy and User Control in the Age of Analytics. Northwestern Journal of Technology and Intellectual Property, 11, [xxvii]-274.
van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society; Newcastle upon Tyne, 12(2), 197–208.
Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010
Wu, Z., Wu, J., Khabsa, M., Williams, K., Chen, H. H., Huang, W., … Giles, C. L. (2014). Towards building a scholarly big data platform: Challenges, lessons and opportunities. In IEEE/ACM Joint Conference on Digital Libraries (pp. 117–126). https://doi.org/10.1109/JCDL.2014.6970157
cyber security experts say that weaving your personal and professional lives together via a work laptop is risky business — for you and the company. Software technology company Check Point conducted a survey of over 700 IT professionals which revealed that nearly two-thirds of IT pros believed that recent high-profile breaches were caused by employee carelessness.
DON’T: Save personal passwords in your work device keychain.
DON’T: Make off-color jokes on messaging software.
DON’T: Access free public wi-fi while working on sensitive material.
DON’T: Allow friends or non-IT department colleagues to remotely access your work computer.
DON’T: Store personal data.
DON’T: Work on your side hustle while at the office.
a report from ISACA, a nonprofit association focused on knowledge and practices for information systems. The 2017 State of Cyber Security Study surveyed IT security leaders around the globe on security issues, the emerging threat landscape, workforce challenges and more.
53 percent of survey respondents reported a year-over-year increase in cyber attacks;
62 percent experienced ransomware in 2016, but only 53 percent have a formal process in place to address a ransomware attack;
78 percent reported malicious attacks aimed at impairing an organization’s operations or user data;
Only 31 percent said they routinely test their security controls, while 13 percent never test them; and
16 percent do not have an incident response plan.
65 percent of organizations now employ a chief information security officers, up from 50 percent in 2016, yet still struggle to fill open cyber security positions;
48 percent of respondents don’t feel comfortable with their staff’s ability to address complex cyber security issues;
More than half say cyber security professionals “lack an ability to understand the business”;
One in four organizations allot less than $1,000 per cyber security team member for training; and
About half of the organizations surveyed will see an increase in their cyber security budget, down from 61 percent in 2016.
IoT to Represent More Than Half of Connected Device Landscape by 2021
Section 702 — that authorizes them to monitor some Americans’ communications without a warrant.
The spy agencies are supposed to “minimize” details about people swept up in what they call such “incidental collection,” and they say their practices are regularly vetted by Congress and the Foreign Intelligence Surveillance Court.
shows that state and federal laws, as well as industry self-regulation, have failed to keep up with a growing education technology industry.
One-third of all K–12 students in the United States use school-issued devices running software and apps that collect far more information on kids than is necessary.
Resource-poor school districts can receive these tools at deeply discounted prices or for free, as tech companies seek a slice of the $8 billion ed tech industry. But there’s a real, devastating cost — the tracking, cataloging and exploitation of data about children as young as 5 years old.
Our report shows that the surveillance culture begins in grade school, which threatens to normalize the next generation to a digital world in which users hand over data without question in return for free services
EFF surveyed more than 1,000 stakeholders across the country, including students, parents, teachers and school administrators, and reviewed 152 ed tech privacy policies.
“Spying on Students” provides comprehensive recommendations for parents, teachers, school administrators and tech companies to improve the protection of student privacy. Asking the right questions, negotiating for contracts that limit or ban data collection, offering families the right to opt out, and making digital literacy and privacy part of the school curriculum are just a few of the 70-plus recommendations for protecting student privacy contained in the report.