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

Thu Apr 6 11:43:55 EAT 2017

https://www.torproject.org/projects/torbrowser.html.en

On Mon, Apr 3, 2017 at 9:31 AM, Kelvin Kariuki via kictanet <
kictanet at lists.kictanet.or.ke> wrote:

> 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-m
>>> etadata-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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>>
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>> ailman/options/kictanet/kelvinkariuki89%40gmail.com
>>
>> The Kenya ICT Action Network (KICTANet) is a multi-stakeholder platform
>> 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
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>
>
>
> --
> Best Regards,
>
> Kelvin Kariuki
> Twitter Handle: @teacherkaris
> Alt email: kkariuki at mmu.ac.ke
> Mobile: +2547 29 385 557
>
>
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>
> Unsubscribe or change your options at https://lists.kictanet.or.ke/
> mailman/options/kictanet/anderson.levi%40gmail.com
>
> The Kenya ICT Action Network (KICTANet) is a multi-stakeholder platform
> 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.
>
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