[kictanet] Hakuna Metadata (1) - Exploring (one's) browsing history.

Mon Apr 3 09:31:43 EAT 2017

Noma sana! Everyone needs to be using a VPN when browsing as articulated in
this article (
https://qz.com/945261/how-to-get-a-personal-vpn-and-why-you-need-one-now/?utm_source=qzfb)
in order to guard your privacy. Below is a snapshot of a part of the
article that I found useful:

"Opera is a popular web browser that comes with some excellent privacy
features, like a free built-in VPN and a free ad blocker (and as you may
know, ads can spy on you).

If you just want a secure way to browse the web without ISPs being able to
easily snoop on you and sell your data, Opera is a great start. Let’s
install and configure it real quick. This takes less than 5 minutes."

On Mon, Apr 3, 2017 at 8:13 AM, Rosemary Koech-Kimwatu via kictanet <
kictanet at lists.kictanet.or.ke> wrote:

> Hi Nanjira,
>
> This is really insightful and thought provoking.
>
> Still digesting...
>
> Kind regards,
>
> Rosemary Koech-Kimwatu
> Advocate-FinTech and ICT Policy
> +254 718181644
> On Apr 2, 2017 10:12 PM, "Nanjira Sambuli via kictanet" <
> kictanet at lists.kictanet.or.ke> wrote:
>
>> Highly enjoyable, and insightful read on metadata (and lack of
>> protections thereof) to profile you for advertising etc.
>> In the context of no privacy laws or regulations, our ISPs know quite a
>> bit about us, and who knows what/how that info is (ab)used...
>>
>> http://www.privacypies.org/blog/metadata/2017/02/28/hakuna-
>> metadata-1.html
>>
>> Hakuna Metadata (1) - Exploring the browsing history.
>>
>> Since I joined European Digital Rights (EDRi) in September 2016, one of
>> most hottest topics that is being discussed in the Brussels bubble is the
>> review of ePrivacy rules (ePR). As a complementary instrument to the
>> General Data Protection Regulation (GDPR), ePR mainly deals with data
>> protection and privacy in the electronic communications sector, such as the
>> tracking of users when they browse the internet. Since the GDPR has been
>> already finalized, advocacy around the ePR is probably the last chance to
>> defend European citizens digital rights. One of my key responsibilities as
>> the Ford-Mozilla Open Web Fellow
>> <https://advocacy.mozilla.org/en-US/open-web-fellows/fellows2016> is to
>> bring practical understanding to policy/political debate
>> <https://storyengine.io/stories/decentralization/joe-mcnamee/>, and I
>> agreed with Joe that I will work on the issues that needs more technical
>> clarifications. One such blur area in the ePR happens to be “metadata and
>> the impact on privacy”. So, this article is an explainer about the power of
>> metadata and the reason why we need stronger policies in that context.
>> What is metadata?
>>
>> Without getting too much into details about the technical or EU
>> definitions <http://bit.ly/2lGtClu> of metadata, let us simply
>> understand it as the data about the data. The table below illustrates the
>> difference between the *data and the metadata*.
>>
>> [image: Table 1: Data vs Metadata]
>>
>> Often we see that the data is considered to be sensitive and as a
>> personal property it has to be protected. It is possible to protect your
>> data using encryption technologies, for example GNU Privacy Gaurd (GPG) for
>> emails. On the other hand, metadata is not treated to be very sensitive and
>> for the same reason there are not many methods to encrypt it. It is due to
>> the technical shortcoming of the basic building blocks of Internet Protocol
>> (IP) stack. It does make sense to not encrypt the metadata right? Because
>> if we encrypt the sender information on an email, your email client
>> wouldn’t know whom to send it to.
>>
>> When the internet protocols were built, the intention was merely to
>> establish a communication channel to connect the world. At that point of
>> time, there were no much threats from government spying agencies, mass
>> surveillance programs or from the advertisers. However, today we live in a
>> world where everything we do on the Internet is being tracked and thus
>> putting our privacy for sale on the data market. Even though the metadata
>> has been a gold mine for Internet Service Providers (ISPs),
>> Telecommunication providers from past two decades, the privacy risks of the
>> metadata started to be a debatable topic since the Snowden revelations
>> <http://uk.businessinsider.com/nsa-document-metadata-2016-12?r=US&IR=T>.
>> Here are some of the quotes about the power of metadata from former big
>> shots of government spying programs.
>> ------------------------------
>> ------------------------------
>>
>> *“Metadata absolutely tells you everything about somebody’s life. If you
>> have enough metadata, you don’t really need content.”* - *Stewart Baker*,
>> Ex- NSA General Counsel
>>
>> *“We kill people based on metadata.”* - *Michael Hayden*, former
>> director of the NSA and ex- CIA
>> ------------------------------
>> ------------------------------
>>
>> Metadata by its virtue is not invented to help privacy invaders; instead
>> it was intended to fasten the process of classification and indexing of any
>> kind of bulk data, without looking at the data itself. So, by definition,
>> metadata enforces data protection by letting someone process the data,
>> without even looking at the content inside. However, that is also the
>> fastest way to profile the whole internet users, right? Earlier in October
>> 2015, Share Lab presented this <https://labs.rs/en/metadata/> piece of
>> investigative journalism which articulates the hidden power of email
>> metadata. Indeed, it is scary to see what one can understand about personal
>> behavior just from the “To”, “From”, “Subject” and “Timestamp” fields.
>> Other than the scary use-cases, there are a handful of projects such as
>> Proofmode
>> <https://guardianproject.info/2017/02/24/combating-fake-news-with-a-smartphone-proof-mode/>
>> (earlier known as Informacam - CameraV
>> <https://guardianproject.info/apps/informacam/>) which harness the power
>> of metadata for combating against fake news. However, the number of
>> projects which exploits that power for advertisement tracking and
>> surveillance outbeats the genuine use cases of metadata.
>> Browsing history and the potential threat actors
>>
>> Modern browsers such as Firefox, Google Chrome, Opera and Internet
>> Explorer stores the browsing history to provide a user-friendly browsing
>> experience. By default, these browsers store the history of all the
>> previously visited websites, cached copy of the websites, form filling
>> history, cookie information and also bookmarks. Depending on the operating
>> system and the browser, these information will be stored in a specific
>> location on the hard disk of your computer in a lightweight database. Some
>> of us rant about this default nature of the browsers, as it compels users
>> to manually opt-out of browsing history storing mechanisms and the privacy
>> concerns associated with it. Browser history - specifically the website
>> information and cached copy has its own advantage in terms of usability:
>>
>>    1. Automatic completion/suggestion of previously visited URLs.
>>    2. Locally cached copies of the previously visited websites to boost
>>    up the browsing speed, which is very helpful when the Internet connection
>>    is very slow.
>>
>> At this point, it is obvious that our browsing history is accessible to
>> our browsers, which is why it is highly recommended to use open-source
>> trustworthy browsers such as Mozilla Firefox
>> <https://www.mozilla.org/en-US/firefox/products/> ,which protects and
>> respects your privacy. Whereas if you are using other browsers from the
>> companies which are themselves the data brokers and advertisers, you end up
>> giving away your browsing history to get tracked. So, assuming that we
>> trust our browsers, let us exclude it from being a threat actor in our
>> model.
>> Entity Access to history Comments
>>
>> Malware in the computer
>>
>> Full
>>
>> Any program which has adequate privileges to start a browser process and
>> browse the web potentially has the capacity to leak it. Such malwares
>> <http://www.spamfighter.com/News-20261-Horrid-Piece-of-Android-Malware-Monitors-Browser-History-Texts-and-Banking-Information.htm> have
>> a high demand in the darknet.  Other than that, there are browser
>> hijacking malware <https://en.wikipedia.org/wiki/Browser_hijacking&> which
>> pollutes your history
>>
>> Wifi Hotspot
>>
>> Full
>>
>> Using captive Wi-Fi
>> <http://ieee-security.org/TC/SPW2016/MoST/slides/s2/t1.pdf> is a common
>> practice in many places, especially when using public hotspots
>> <http://qurinet.ucdavis.edu/pubs/conf/Ningning_INFOCOM13.pdf>.
>>
>> Internet Service Providers (ISPs)
>>
>> Almost full
>>
>> ISPs can seek many insights, even when the traffic is encrypted
>> <https://www.teamupturn.com/reports/2016/what-isps-can-see>. Have a look
>> at “How Internet sees you
>> <https://events.ccc.de/congress/2010/Fahrplan/attachments/1791_27C3-JeroenMassar-HowTheInternetSeesYou.pdf>
>> ”
>>
>> HTTP: The ISP knows which pages you're visiting and could see the data
>> you send and receive.
>>
>> HTTPS: The ISP knows which domain you've visited but not the URL
>> parameters, and not the contents of any data you send or receive.
>>
>> Domain Name Service (DNS) Providers
>>
>> Partial
>>
>> Only the domain name queries and not complete URL.
>>
>> Cookies (tracking, advertising and profiling companies)
>>
>> Partial to almost full (depending on who’s cookie it is)
>>
>> Based on cookie origin policies, cookies from Website A can collect the
>> history related to that.
>>
>> Websites that you visit
>>
>> Partial
>>
>> Any websites that you visit would obviously know that you have visited
>> them.
>>
>> *Table 2: Access to browsing history*
>>
>> In spite of the clear privacy implications, there is no clarity under the
>> law about whether browsing history (more specifically the URLs) is to be
>> protected as content or non-content metadata. Most of the lobbyists express
>> their dissatisfaction about the changes between leaked and the official
>> proposals of ePR. Out of many other concerns, the most questionable parts
>> of the ePrivacy Regulations, at least for me is the permitted usage and
>> exceptions <http://ec.europa.eu/newsroom/dae/document.cfm?doc_id=41461>
>> of contents of communication. Things are a bit more complicated than that
>> on two levels. Firstly, there are various cross-cutting issues (consent,
>> tracking, ISPs, “value-added services”, etc…) where metadata analysis comes
>> up. Exceptions for web analytics could imply serious privacy concerns
>> without stronger guarantees of statistical privacy.
>>
>> Without getting much into that debate, let us explore browsing history as
>> it provides a rich source of metadata of our daily interactions with the
>> internet world. For the sake of simplicity and for understanding the power
>> of this chunk of metadata, let us assume a malicious ISP (who can
>> completely or partially see our browsing metadata) who does not respect the
>> privacy policies to be the threat actor.
>> Analytics about the web history
>>
>> Based on the browsing history contained in my computer, below is a simple
>> analytics of the website domain names that I have visited the most. Like
>> any other “normal” internet user I have used Google as the search engine;
>> spent ample amount of time on social media sites such as Twitter, Facebook
>> and LinkedIn; watched videos over Youtube; used Wikipedia as the primary
>> source of information; shopped on Amazon; sought programming help over
>> Github and Stackoverflow; and so on.
>>
>> [image: Figure 1: Most visited domain names]
>>
>> *Figure 1: Most visited domain names*
>>
>> According to ePR and as per the global norms of deducing useful insight
>> of the users, ISPs can use such analytics for their survey purposes. Under
>> genuine use cases, these kind of statistics are helpful for fine-tuning the
>> bandwidth for specific websites that are used more by the users. Even
>> though the top 20 websites remain the same across all parts of the world,
>> depending on demographics and social structure of a region, the websites
>> that will appear after the top 20 are not always the same. Your
>> contribution to big data analytics, starts right from here - just by
>> contributing the domain names of the websites that you have visited. The
>> same chunk can be used for profiling you as well. May be these websites in
>> Figure 1, is most common to all and does not really profile as you
>> different. But, imagine some of the porn sites or your favourite political
>> parties web page! Well, that makes you little different than others right?
>>
>> Figure 2 belows shows the suffixes or more technically the top-level
>> domains (TLD) of the websites that I have visited the most. In many cases
>> TLDs represent the countries that the websites are affiliated with. Also,
>> websites like Google change the TLDs depending on the country from which
>> you are browsing their website. For instance, even if you typed
>> www.google.com from Belgium, it will be redirected to www.google.be
>> automatically. Based on Figure 2, one can easily tell that I have
>> connections with Finland (.fi), Belgium (.be), India (.in and .co.in)
>> and some academic affiliation (.edu). While your ISP will obviously know
>> these information, imagine the case when you are travelling!
>>
>> [image: Figure 2: Suffix (TLDs) of most visited websites]
>>
>> *Figure 2: Suffix (TLDs) of most visited websites*
>>
>> Even you are in a foreign country, you still visit websites related to
>> your home country. So, along with the ISP of the foreign country, your
>> geographic affiliation or affinity is now evident to the DNS providers as
>> well. At this point, you have contributed second chunk of information to
>> the big data and profiling to two of the entities which can collect data
>> about you.
>> Browsing patterns
>>
>> If the internet traffic is HTTP, everything will be transmitted in plain
>> text. So, ISPs can see full path of the URL (http://www.facebook.com/zuck
>> ). Whereas, when it is HTTPs only partial path is visible (
>> https://www.facebook.com/) to the ISPs. To know more about how Internet
>> works, refer to EDRi’s paper
>> <https://edri.org/papers/how-the-internet-works/> on the same topic.
>>
>> [image: Figure 3: Number of unique URLs visited over time]
>>
>> *Figure 3: Number of unique URLs visited over time*
>>
>> Since the full path of URL is visible to the ISPs when your traffic is
>> not encrypted, they can start analysing your behavior online. Figure 3
>> represents a graph of the total number of unique websites that have visited
>> over time based on my browsing history. As one can see, I visit 10-150
>> unique URLs on an average over the period of November 2015 to January 2017.
>> Some peaks in the graph beyond this range shows a lot of anomalies in my
>> browsing pattern. These anomalies could potentially indicate certain
>> specific events of my life. It could be increased workload, planning my
>> travel, searching for a job or anything that you can imagine.
>>
>> [image: Figure 4: Heatmap of browsing pattern - unique URLs visited over
>> time]
>>
>> *Figure 4: Heatmap of browsing pattern - unique URLs visited over time*
>>
>> Another way of looking at the browsing patterns is by plotting a heatmap
>> of the same data i.e. the number of unique URLs visited over time as shown
>> in Figure 4. While Figure 3 shows the anomalies in the browsing pattern,
>> the heatmap gives a snapshot of the lifestyle in an easily understandable
>> manner.
>>
>> [image: Figure 5: Heatmap of browsing pattern - sleeping (idle) and
>> leisure time]
>>
>> *Figure 5: Heatmap of browsing pattern - sleeping (idle) and leisure time*
>>
>> There are consistent patterns in the lower half and the upper quarter of
>> the graph. Even within those patterns, we can see two different sets, which
>> depicts my work time browsing and after-work leisurely activities as it
>> fades out from 20:00 hour onwards. In the figure 5, from 12:00 AM till
>> 07:00 AM, there is a constant strip of dark patch which represents less
>> activity over the internet, or in other words it is the time when I sleep.
>>
>> [image: Figure 6: Heatmap of browsing pattern - travel]
>>
>> *Figure 6: Heatmap of browsing pattern - travel*
>>
>> As highlighted in the figure 6, there are certain patches within the
>> strip of my sleeping pattern. When correlated with the change in name
>> suffixes (with reference to figure 2), it was found out to be work-related
>> travels. In other words, I had travelled to a different timezone and
>> continued to work from 9.00 AM to 7:00 PM as I have done on any other
>> regular day.
>>
>> [image: Figure 7: Heatmap of browsing pattern - Holiday season]
>>
>> *Figure 7: Heatmap of browsing pattern - Holiday season*
>>
>> If we zoom in the graph more (As represented in figure 7), there are
>> patterns which show high number of browsing, a patch of almost no
>> activities even during the regular working hours, then a sudden increase in
>> browsing activities and finally resuming to normal working hour pattern.
>> This depicts that I planned for my holiday (checking into flights,
>> confirming hotel booking, etc.), took a break from work, returned from the
>> holiday (sudden increase is possibly due to following up on emails and
>> activities that I might have missed during my trip) and finally resuming my
>> work.
>>
>> So, at this point, one can know about my working hours, sleep time,
>> work-related travel and my holiday schedules just using my browsing
>> metadata. That is quite a lot of information about me retrieved just from
>> the metadata right?
>> Potential adwords
>>
>> As mentioned earlier, browsing history falls into the grey area of
>> whether to be treated and protected as content data or as the non-content
>> metadata. Unlike many other metadata, where it is not possible to retrieve
>> the complete content data just by using the metadata associated with it, it
>> is possible to retrieve all the contents of the websites that you have
>> visited by crawling over the list of URLs from your browsing history.
>> Whether or not it happens in reality, to avoid giving the list of URLs
>> directly to advertisers, the ISPs can automate their analytics system to
>> crawl over the list to seek insight on what you might have seen while
>> browsing. By giving away just the keywords deduced from the websites that
>> you have visited to the advertisers, the ISPs can potentially bypass the
>> privacy laws by claiming it as anonymized.
>>
>> [image: Figure 8: Wordcloud generated by crawling over the list of URLs
>> from the browsing history]
>>
>> *Figure 8: Wordcloud generated by crawling over the list of URLs from the
>> browsing history*
>>
>> Figure 8 represents the wordcloud generated by crawling over the most
>> visited websites by me. Not so surprisingly, being a security and privacy
>> researcher, I can see those words in this cloud, along with other keywords
>> related to my identity - both from professional and personal life. This
>> cloud was derived by excluding all the social media and search engine
>> related URLs.
>>
>> Yet another buzzwords which we hear often these days is - “Data mining”
>> and “machine learning”. Data Mining refers to seeking useful insights
>> programmatically from the collected bulk data, whereas machine learning is
>> to use that insight for data-driven decision making. One of the features of
>> these methods known as Named Entity Recognition (NER)
>> <https://en.wikipedia.org/wiki/Named-entity_recognition> which allows to
>> classify the text into categories such as organizations, persons and
>> locations.
>>
>> [image: Figure 9: Wordcloud generated by Name-Entity Recognition -
>> Organizational entities]
>>
>> *Figure 9: Wordcloud generated by Name-Entity Recognition -
>> Organizational entities*
>>
>> If we run NER algorithms on the text retrieved by crawling over the list
>> of URLs that you have visited, it provides more clarity to the keywords
>> that can be potentially generated. Figure 9 shows the keywords related to
>> organizational entities from the websites that I have visited. This narrows
>> down my generic profile to target me on the keywords found in this cloud.
>> For example, I could be a potential customer for insurance companies,
>> University and management related jobs.
>>
>> Further down the line, figure 10 represents the names of the people found
>> in the websites that I have visited the most. Surprisingly, I turned out to
>> be the self-obsessed person who visits websites of his own or the websites
>> that talks about himself. In the Person names cloud, I can see some of my
>> academic co-authors, role models or the people whom I follow. Imagine that
>> there are the names of Tim Cook or Steve Jobs! I am probably a potential
>> customer for Apple! So, the list of adwords targeted towards me could
>> include Apple products here onwards.
>>
>> [image: Figure 10: Wordcloud generated by Name-Entity Recognition -
>> Person names]
>>
>> *Figure 10: Wordcloud generated by Name-Entity Recognition - Person names*
>>
>> How about my next travel destination? Can it be predicted from my web
>> history? Possible yes - it could be Brazil, China or Singapore!
>>
>> [image: Figure 11: Wordcloud generated by Name-Entity Recognition -
>> Location entities]
>>
>> *Figure 11: Wordcloud generated by Name-Entity Recognition - Location
>> entities*
>>
>> As the Figure 11 represents, I might have visited the websites which
>> contained those locations which could probably be my next travel
>> destination. Even without doing any fancy machine learning processing, I
>> could attest that these were actually some of the places that I am planning
>> to visit!
>>
>> As mentioned before, if you are using HTTP, the ISPs can see the full URL
>> path in clear text. Along with them, the websites that you visit will
>> obviously have to know that full path to deliver you exactly what you are
>> looking for.
>>
>> If you have searched for “vegetarian restaurants in Brussels”in Google ,
>> your Google query URL will be http://www.google.be/search?q=
>> vegetarian+restaurant+brussel
>> <https://www.google.be/search?q=vegetarian+restaurant+brussel>. Assuming
>> that the ISPs will use the keywords you are searching to profile you again,
>> it makes their job of deriving the adwords for your future targeted
>> advertisements much more easier.
>>
>> *Please note that Google auto-redirects HTTP traffic to HTTPS, however,
>> for the sake of simplicity let us ignore that. Instead, there are many
>> malicious things (like “man-in-the-middle”) that an ISPs can do in
>> cooperation with advertisers to know more information even from the HTTPS
>> traffic.*
>>
>> [image: Figure 12: Word frequency graph of Google search keywords]
>>
>> *Figure 12: Word frequency graph of Google search keywords*
>>
>> Figure 12 represents the most searched words by me on Google. From this
>> graph, it is evident that I use Python programming language, Latex for
>> writing reports, use a computer with Ubuntu as the operating system,
>> research on security/privacy, and so on. This itself along with the
>> previous world cloud would be enough to profile me.
>>
>> So, at this point, the ISPs know what makes you highly likely to click an
>> advertisement link!
>> Pseudo social sphere
>>
>> Unlike the metadata related to emails and phone call logs, the browsing
>> history can be treated as one-dimensional metadata. Because, it is just the
>> metadata about what you have browsed and it does not contain the influence
>> of other people’s interaction with you. On the other hand, email and phone
>> call metadata contains the interaction you have done with others, along
>> with the interactions done by others with you.
>>
>> However, it is possible to seek insight on your affinity towards the
>> people within your social circle using the one-dimensional browsing history
>> metadata. For example, you will visit your close friends social media
>> profile more frequently than you visit your ex-colleague’s profile whom you
>> know from first job. You might have visited the profile of your friend from
>> the university more recently and frequently, than you visit your friend
>> from high school. By capturing the number of visit counts and frecency
>> (frequency + recency) from your browsing history, it is possible to
>> reconstruct a pseudo social sphere (figure 13) , and thereby converting the
>> browsing history to a two-dimensional data source.
>>
>> [image: Figure 13: Representation of pseudo-social sphere derived from
>> social media related URLs]
>>
>> *Figure 13: Representation of pseudo-social sphere derived from social
>> media related URLs*
>>
>> We all have different social circles - family members, childhood/high
>> school friends, friends from work place, ex-colleagues , etc. Our affinity
>> towards them is not necessarily unique. Even though they are not directly
>> connected with each other, it is highly likely that our affinity towards
>> them is similar. By capturing the social media URLs (Facebook and Twitter),
>> Figure 13 represents one such social sphere. In circle 1, I saw a family
>> member and my best friend; in circle 2, one of my colleagues, highschool
>> friend and a family member were seen. This means I weigh them differently,
>> but they can be grouped based on my affinity towards them.
>>
>> Just by knowing the browsing history, now the ISPs can tell who are my
>> close friends, how much do they matter to me and who all have equal
>> importance in my life.
>> To summarize:
>>
>> I built a small/ naive tool to replicate the similar graphs shown in this
>> article for almost anyone who is a Linux+Firefox user, browses Internet
>> including social media like anyone else and most importantly stores the
>> browsing history for a decent period of time. While making this tool as
>> generic and simple as possible, I had to omit digging more information that
>> could have been gathered from my own browsing history and exclude use of
>> APIs (as they require individual users to obtain the API tokens). However,
>> to know more about what browsing history could reveal about your
>> personalities, refer to the case study by Share Lab. This provides lot more
>> insights on what one can dig from your browsing history.
>>
>> Whether or not the culprit ISPs as depicted in this article evade your
>> privacy by doing all these analytics, it is indeed important to realize the
>> power of metadata and your contribution to big data processing in the wild.
>> Since privacy of the metadata can not be protected by merely encrypting it,
>> we need stronger policies to defend our digital rights.
>>
>> The tool which I call as Haukana metadata can be downloaded from *here*
>> <https://github.com/sidtechnical/hakuna-metadata-1>. Once you download
>> it, follow these instruction:
>>
>>    - Unzip the folder a right click on a blank area Click on “open in
>>    terminal”.
>>    - In the terminal, type *sh requirements* and press *Enter*.
>>    - This will download all the necessary modules needed to run the tool.
>>    - Once it is completed, type *python tool.py* and press *Enter*.
>>    - It will take some time to process your browsing history. So, be
>>    patient until it opens a new browser tab as a result. Everything will be
>>    processed within your computer and hence, the tool does not send the data
>>    anywhere.
>>    - The newly opened tab will contain some instructions and links to
>>    the visualizations derived from your browsing history.
>>    - Please note that these graphs are interactive as shown here
>>    <https://github.com/sidtechnical/sidtechnical.github.io/blob/master/assets/images/bh_heatmap.gif?raw=true>,
>>    here
>>    <https://github.com/sidtechnical/sidtechnical.github.io/blob/master/assets/images/bh_anamoly.gif?raw=true>,
>>    here
>>    <https://github.com/sidtechnical/sidtechnical.github.io/blob/master/assets/images/bh_search.gif?raw=true>
>>    or here
>>    <https://github.com/sidtechnical/sidtechnical.github.io/blob/master/assets/images/bh_soccirc.gif?raw=true>
>>    .
>>    - It is important to note that some of the functionalities may not
>>    work as it is shown in this article, mainly because there are no reference
>>    data about browsing history. So, I had to build it based on my own browsing
>>    history.
>>    - It goes without saying that the code is open source, and any
>>    contribution to the code to improvise and add more functionalities are more
>>    than welcome. Even otherwise, in case of issues, do not hesitate to contact
>>    me, either by sending an email with subject line “Hakuna Metadata” to
>>    sidtechnical at gmail.com or by raising an issue on Github.
>>
>>
>>
>> Regards,
>> Nanjira.
>>
>> Sent on the move.
>>
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> for people and institutions interested and involved in ICT policy and
> regulation. The network aims to act as a catalyst for reform in the ICT
> sector in support of the national aim of ICT enabled growth and development.
>
> KICTANetiquette : Adhere to the same standards of acceptable behaviors
> online that you follow in real life: respect people's times and bandwidth,
> share knowledge, don't flame or abuse or personalize, respect privacy, do
> not spam, do not market your wares or qualifications.
>

-- 
Best Regards,

Kelvin Kariuki
Twitter Handle: @teacherkaris
Alt email: kkariuki at mmu.ac.ke
Mobile: +2547 29 385 557
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