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• What is data anonymization in web analytics ?

  11 février 2020, par Joselyn Khor — Analytics Tips, Privacy
  Collecting information via web analytics platforms is needed to help a website grow and improve. When doing so, it’s best to strike a balance between getting valuable insights, and keeping the trust of your users by protecting their privacy.

  This means not collecting or processing any personally identifiable information (PII). But what if your organisation requires you to collect PII ?

  That’s where data anonymization comes in.

  

  What is data anonymization ?

  Data anonymization makes identifiable information unidentifiable. This is done through data processing techniques which remove or modify PII data. So data becomes anonymous and can’t be linked to any individual.

  In the context of web analytics, data anonymization is handy because you can collect useful data while protecting the privacy of website visitors.

  Why is data anonymization important ?

  Through modern threats of identity theft, credit card fraud and the like, data anonymization is a way to protect the identity and privacy of individuals. As well as protect private and sensitive information of organisations. 

  Data anonymization lets you follow the many laws around the world which protect user privacy. These laws provide safeguards around collecting personal data or personally identifiable information (PII), so data anonymization is a good solution to ensure you’re not processing such sensitive information.

  In some cases, implementing data anonymization techniques means you can avoid having to show your users a consent screen. Which means you may not need to ask for consent in order to track data. This is a bonus as consent screens can annoy and stop people from engaging with your site.

  GDPR and data anonymization

  

  The GDPR is a law in the EU that limits the collection and processing of personal data. The aim is to give people more control over their online personal information. Which is why website owners need to follow certain rules to become GDPR compliant and protect user privacy. According to the GDPR, you can be fined up to 4% of your yearly revenue for data breaches or non-compliance. 

  In the case of web analytics, tools can be easily made compliant by following a number of steps. 

  This is why anonymizing data is a big deal.

  Anonymized data isn’t personal data according to the GDPR : 

  “The principles of data protection should therefore not apply to anonymous information, namely information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable.”

  This means, you still get the best of both worlds. By anonymizing data, you’re still able to collect useful information like visitor behavioural data.

  US privacy laws and data anonymization

  In the US, there isn’t one single law that governs the protection of personal data, called personally identifiable information (PII). There are hundreds of federal and state laws that protect the personal data of US residents. As well as, industry-specific statutes related to data privacy, like the California Consumer Privacy Act (CCPA) and the Health Insurance Portability and Accountability Act (HIPAA).

  Website owners in the US need to know exactly what laws govern their area of business in order to follow them.

  A general guideline is to protect user privacy regardless of whether you are or aren’t allowed to collect PII. This means anonymizing identifiable information so your website users aren’t put at risk.

  Data anonymization techniques in Matomo Analytics

  If you carry these out, you won’t need to ask your website visitors for tracking consent since anonymized data is no longer considered personal data under the GDPR.

  • Anonymize previously tracked raw data – like visitors/visits
  • Anonymize Visitor IP addresses
  • Anonymize geo-location information (country, region, city)
  • Pseudonymize User ID ( if you don’t want this data to land on your Matomo server, the solution is simply to not set the User ID. Then Matomo will use the normal anonymized IDs)
  • Data can also be anonymized before it’s even sent to Matomo
  • Learn how to further configure privacy settings in a web analytics tool like Matomo
  • 11 ways Matomo Analytics helps you to protect your visitors privacy

  The techniques listed above make it easy for you when using a tool like Matomo, as they are automatically anonymized.

  Tools like Google Analytics on the other hand don’t provide some of the privacy options and leave it up to you to take on the burden of implementation without providing steps.

  Data anonymization tools

  If you’re a website owner who wants to grow your business or learn more about your website visitors, privacy-friendly tools like Matomo Analytics are a great option. By following the easy steps to be GDPR compliant, you can anonymize all data that could put your visitors at risk.

• ffmpeg concatenate results in bad/garbled output for second input file

  28 février 2017, par Dan

  I am trying to concatenate two videos together using the ffmpeg concat demuxer : a 5-second intro splash and a 60-second main/body video. I understand that the encoding needs to be the same on both files for this to work. I think they are, but the resulting output shows the intro just fine, followed by a totally garbled body. What am I missing ?

  ffmpeg -f concat -i concat.txt -c copy output.mp4

  Contents of concat.txt :

  file 'intro.mp4'
  
  file 'body.mp4'

  and the output of ffprobe for both input files :

  intro.mp4

  ffprobe version 3.2.4 Copyright (c) 2007-2017 the FFmpeg developers
  
    built with Apple LLVM version 7.0.2 (clang-700.1.81)
  
    configuration: --prefix=/usr/local/Cellar/ffmpeg/3.2.4 --enable-shared --enable-pthreads --enable-gpl --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-libmp3lame --enable-libx264 --enable-libxvid --enable-opencl --disable-lzma --enable-vda
  
    libavutil      55. 34.101 / 55. 34.101
  
    libavcodec     57. 64.101 / 57. 64.101
  
    libavformat    57. 56.101 / 57. 56.101
  
    libavdevice    57.  1.100 / 57.  1.100
  
    libavfilter     6. 65.100 /  6. 65.100
  
    libavresample   3.  1.  0 /  3.  1.  0
  
    libswscale      4.  2.100 /  4.  2.100
  
    libswresample   2.  3.100 /  2.  3.100
  
    libpostproc    54.  1.100 / 54.  1.100
  
  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'intro.mp4':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf57.56.101
  
    Duration: 00:00:05.04, start: 0.000000, bitrate: 73 kb/s
  
      Stream #0:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 960x720 [SAR 1:1 DAR 4:3], 69 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
  
      Metadata:
  
        handler_name    : VideoHandler

  body.mp4

  ffprobe version 3.2.4 Copyright (c) 2007-2017 the FFmpeg developers
  
    built with Apple LLVM version 7.0.2 (clang-700.1.81)
  
    configuration: --prefix=/usr/local/Cellar/ffmpeg/3.2.4 --enable-shared --enable-pthreads --enable-gpl --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-libmp3lame --enable-libx264 --enable-libxvid --enable-opencl --disable-lzma --enable-vda
  
    libavutil      55. 34.101 / 55. 34.101
  
    libavcodec     57. 64.101 / 57. 64.101
  
    libavformat    57. 56.101 / 57. 56.101
  
    libavdevice    57.  1.100 / 57.  1.100
  
    libavfilter     6. 65.100 /  6. 65.100
  
    libavresample   3.  1.  0 /  3.  1.  0
  
    libswscale      4.  2.100 /  4.  2.100
  
    libswresample   2.  3.100 /  2.  3.100
  
    libpostproc    54.  1.100 / 54.  1.100
  
  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'body.mp4':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf57.56.101
  
    Duration: 00:00:36.84, start: 0.000000, bitrate: 5615 kb/s
  
      Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 960x720 [SAR 1:1 DAR 4:3], 5613 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
  
      Metadata:
  
        handler_name    : VideoHandler

  Output of ffmpeg :

  ffmpeg version 3.2.4 Copyright (c) 2000-2017 the FFmpeg developers
  
    built with Apple LLVM version 7.0.2 (clang-700.1.81)
  
    configuration: --prefix=/usr/local/Cellar/ffmpeg/3.2.4 --enable-shared --enable-pthreads --enable-gpl --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-libmp3lame --enable-libx264 --enable-libxvid --enable-opencl --disable-lzma --enable-vda
  
    libavutil      55. 34.101 / 55. 34.101
  
    libavcodec     57. 64.101 / 57. 64.101
  
    libavformat    57. 56.101 / 57. 56.101
  
    libavdevice    57.  1.100 / 57.  1.100
  
    libavfilter     6. 65.100 /  6. 65.100
  
    libavresample   3.  1.  0 /  3.  1.  0
  
    libswscale      4.  2.100 /  4.  2.100
  
    libswresample   2.  3.100 /  2.  3.100
  
    libpostproc    54.  1.100 / 54.  1.100
  
  [mov,mp4,m4a,3gp,3g2,mj2 @ 0x7fdda3018000] Auto-inserting h264_mp4toannexb bitstream filter
  
  Input #0, concat, from 'concat.txt':
  
    Duration: N/A, start: 0.000000, bitrate: 69 kb/s
  
      Stream #0:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 960x720 [SAR 1:1 DAR 4:3], 69 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc
  
      Metadata:
  
        handler_name    : VideoHandler
  
  Output #0, mp4, to 'output.mp4':
  
    Metadata:
  
      encoder         : Lavf57.56.101
  
      Stream #0:0(eng): Video: h264 (High) ([33][0][0][0] / 0x0021), yuv420p, 960x720 [SAR 1:1 DAR 4:3], q=2-31, 69 kb/s, 25 fps, 25 tbr, 12800 tbn, 12800 tbc
  
      Metadata:
  
        handler_name    : VideoHandler
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (copy)
  
  Press [q] to stop, [?] for help
  
  [mov,mp4,m4a,3gp,3g2,mj2 @ 0x7fdda3800000] Auto-inserting h264_mp4toannexb bitstream filter
  
  frame= 1047 fps=973 q=-1.0 Lsize=   25299kB time=00:00:41.76 bitrate=4962.8kbits/s speed=38.8x    
  
  video:25286kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.050621%

  As far as I can tell, everything is identical except for the bitrate and language. Setting the language doesn’t seem to make a difference. I’ve tried to transcode the intro to a higher bitrate for the intro but can’t get it to stick in the output. I’m OK with transcoding one of the files (ideally the intro) but don’t know what else to try to change. Thoughts ?

• The complete guide on tracking your websites and web apps into multiple Piwiks and how to do it easily & efficiently

  23 février 2017, par InnoCraft — Community, Development

  Getting the tracking of your website and apps right is crucial to your success as you need to ensure the measured data is meaningful and correct. That’s why we, at InnoCraft, help our clients setting up their web tracking and digital measurement strategy. Some challenges include tracking your analytics data into multiple Piwik services as well as the tracking of single-page websites and web applications (covered in a previous article). In this blog post, we explain how to track your data into multiple Piwik websites correctly.

  Embedding the tracking code

  First of all you need to embed your JavaScript tracking code into your website or app as usual. If you haven’t done this yet : Log in to your Piwik, click on “Administration” in the top right and go to “Tracking Code”. There you have various options to adjust your tracking code to your needs.

  Tracking the same data into different websites

  Let’s assume you have set up the regular JavaScript tracking code and you want to track the same data into a second Piwik website. This second Piwik website can be either on the same Piwik installation or on a different Piwik. To do this, add the following line to your tracking code :

  _paq.push(['addTracker', 'https://$yourPiwikDomain/piwik.php', idSite]);

  It should look like this :

  var u = '//$yourPiwikDomain';
  
  _paq.push(['addTracker', u + '/piwik.php', var idSite = 2]); // adds an additional tracker
  
  _paq.push(['setSiteId', '1']); // configures your regular Piwik tracker
  
  _paq.push(['setTrackerUrl', u + 'piwik.php']);

  This will track the same data into website 1 and website 2 of your Piwik installation. You can also change the domain in addTracker to point it to a different Piwik installation :

  _paq.push(['addTracker', '//$differentPiwikDomain/piwik.php', var idSite = 2]);

  All Piwik tracker methods that you call afterwards will be applied to all trackers. Say you call _paq.push(['disableCookies']); _paq.push(['trackPageView']);, then both methods will be called on all tracker instances assuring they will behave the same and will track the same data into both Piwik websites.

  Tracking different data into different websites

  If you want to track only certain data into one website, and different data into an additional website, you need to configure the trackers differently. For example, you want to enable link tracking only for one tracker, but not for the other. The problem is that calling _paq.push(['enableLinkTracking']); enables link tracking on all of your trackers. To workaround this limitation, you can configure your trackers differently like this :

  window.piwikAsyncInit = function () {
  
      Piwik.on('TrackerSetup', function (tracker) {
  
        if (tracker.getSiteId() == 2 
  
           || tracker.getTrackerUrl() === '//$yourPiwikDomain/piwik.php') {
  
            tracker.enableLinkTracking();
  
          }
  
      });
  
  };

  Now it enables link tracking only for the tracker that is configured for a certain website ID or Piwik domain.

  Accessing a previously generated tracker instance

  When you configure a tracker via _paq.push, you create a so called “Async tracker” because Piwik will be loaded asynchronously and create the tracker instance as soon as it is loaded. If you need to get the instance of such a tracker, you can use the method Piwik.getAsyncTracker(trackerUrl, idSite). This can be useful if you have a single-page website and want to track different data into different websites :

  window.addEventListener('hashchange', function() {
  
      if ('undefined' === typeof Piwik) {
  
          // Piwik might not be loaded yet
  
          return;
  
      }
  
      var tracker1 = Piwik.getAsyncTracker('//$yourPiwikDomain/piwik.php', var idSite = 1);
  
      var tracker2 = Piwik.getAsyncTracker('//$yourPiwikDomain/piwik.php', var idSite = 2);
  
      tracker1.setCurrentUrl('/' + window.location.hash.substr(1));
  
      tracker2.setCurrentUrl('/mywebsite/' + window.location.hash.substr(1));
  
  });

  Tracking different data into multiple Piwik installations without using “_paq”

  Some users prefer to not use _paq.push and instead directly create tracker instances themselves using the method Piwik.getTracker(trackerUrl, idSite) like this :

  window.piwikAsyncInit = function () {
  
      var tracker1 = Piwik.getTracker('//$yourPiwikdomain/piwik.php', var idSite = 1);
  
      tracker1.disableCookies();
  
      var tracker2 = Piwik.getTracker('//$yourPiwikdomain/piwik.php', var idSite = 2);
  
      tracker2.enableLinkTracking();
  
  
  
      tracker1.trackPageView();
  
      tracker2.trackPageView();
  
  };

  We usually don’t recommend creating trackers manually as it is more complicated and you need to make sure to configure trackers in the right order. For example to prevent the setting of any cookies, it is recommended to call disableCookies before calling any other methods. If you want to create your trackers manually and you use any of the following methods, make sure to call them in this order :

  disableCookies(), setAPIUrl(), enableCrossDomainLinking(), setCookiePath(), setCookieDomain(), setDomains(), setUserId(), enableLinkTracking()

  Roll-Up Reporting – the easy and efficient way

  Often users track data into multiple websites because they need aggregated data over all their websites. They want to see all statistics for a single website, but also which pages were viewed across all their websites, or how much traffic they got from a specific website or search engine across all websites. This means they add a second tracker to all their websites and track data not only into the regular Piwik website, but also into one additional website that gives them statistics over all websites. This has several disadvantages :

  • Complexity in getting the tracking code right and the time needed to integrate and maintain it
  • Slower website performance because everything needs to be tracked into several websites. This can decrease your conversions and sales
  • Slower Piwik performance because it has to handle twice as much traffic. This means tracking becomes slower, generating the report becomes slower, and the database gets twice as big

  Luckily, there is a better solution called Roll-Up Reporting. With Roll-Up Reporting, you can get aggregated data over all websites and / or for a group of websites without any of these disadvantages. It lets you create as many Roll-Ups as you wish and you can choose which websites’ data should be aggregated together into a new website.

  We had customers who were able to remove one Piwik tracker because of this feature which resulted in less server costs, a faster website, and a faster Piwik. On top of all these advantages, it also lets you view the Visitor Log, Real-time Map, and other widgets and reports across several websites.

  Questions ?

  If you got any questions, please let us know and get in touch with us. You can find more information about the Piwik JavaScript tracker on the Piwik Developer Zone. There is a section dedicated to Multiple Piwik Trackers.