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• Data Privacy Day 2021 : Five ways to embrace privacy into your business

  27 janvier 2021, par Matomo Core Team — Community, Privacy

  Welcome to Data Privacy Day 2021 !

  This year we are excited to announce that we are participating as a #PrivacyAware Champion for DPD21 through the National Cyber Security Alliance. This means that on this significant day we are in partnership with hundreds of other organisations and businesses to share a unified message that empowers individuals to “Own Your Privacy” and for organisations to “Respect Privacy.”

  "Last year dawned a new era in the way many businesses operate from a traditional office work setting to a remote working from home environment for employees. This now means it’s more important than ever for your employees to understand how to take ownership of their privacy when working online."

  As a Data Privacy Day #PrivacyAware Champion we would like to provide some practical tips and share examples of how the Matomo team helps employees be privacy aware.

  

  Five ways to embrace privacy into your business

  1. Create a privacy aware culture within your business

  • Get leadership involved.
  • Appoint privacy ambassadors within your team. 
  • Create a privacy awareness campaign where you educate employees on your company privacy policy. 
  • Share messages about privacy around the office/or in meetings online, on internal message boards, in company newsletters, or emails. 
  • Teach new employees their role in your privacy culture and reinforce throughout their career.

  2. Organise privacy awareness training for your employees

  • Invite outside speakers to talk to employees about why privacy matters. 
  • Engage staff by asking them to consider how privacy and data security applies to the work they do on a daily basis.
  • Encourage employees to complete online courses to gain a better understanding of how to avoid privacy risks.

  3. Help employees manage their individual privacy

  • Better security and privacy behaviours at home will translate to better security and privacy practices at work. 
  • Teach employees how to update their privacy and security settings on personal accounts.
  • Use NCSA’s privacy settings page to help them get started

  4. Add privacy to the employee’s toolbox

  • Give your employees actual tools they can use to improve their privacy, such as company-branded camera covers or privacy screens for their devices, or virtual private networks (VPNs) to secure their connections.

  5. Join Matomo and we’ll be your web analytics experts

  • At Matomo, ensuring our users and customers that their privacy is protected is not only a core component of the work we do, it’s why we do what we do ! Find out how.

  Want to find out more about data privacy download your free DPD 2021 Champion Toolkit and read our post on “Why is privacy important”.

  Team Matomo

  2021 Data Privacy Day Toolkit

  Your guide to Data Privacy Day, January 28, 2021

  Free Download

• GDPR Compliance and Personal Data : The Ultimate Guide

  22 septembre 2023, par Erin — GDPR

  According to the International Data Corporation (IDC), the world generated 109 zettabytes of data in 2022 alone, and that number is on track to nearly triple to 291 zettabytes in 2027. For scale, that’s one trillion gigs or one followed by 21 zeros in bytes.

  A major portion of that data is generated online, and the conditions for securing that digital data can have major real-world consequences. For example, online identifiers that fall into the wrong hands can be used nefariously for cybercrime, identity theft or unwanted targeting. Users also want control over how their actions are tracked online and transparency into how their information is used.

  Therefore, regional and international regulations are necessary to set the terms for respecting users’ privacy and control over personal information. Perhaps the most widely known of these laws is the European Union’s General Data Protection Regulation (GDPR).

  What is personal data under GDPR ?

  Under the General Data Protection Regulation (GDPR), “personal data” refers to information linked to an identifiable natural person. An “identifiable natural person” is someone directly or indirectly recognisable via individually specific descriptors such as physical, genetic, economic, cultural, employment and social details.

  It’s important to note that under GDPR, the definition of personal data is very broad, and it encompasses both information that is commonly considered personal (e.g., names and addresses) and more technical or specialised data (e.g., IP addresses or device IDs) that can be used to identify individuals indirectly.

  Organisations that handle personal data must adhere to strict rules and principles regarding the processing and protection of this data to ensure individuals’ privacy rights are respected and upheld.

  Personal data can include, but is not limited to, the following :

  1. Basic Identity Information : This includes a person’s name, government-issued ID number, social address, phone number, email address or other similar identifiers.
  2. Biographical Information : Details such as date of birth, place of birth, nationality and gender.
  3. Contact Information : Information that allows communication with the individual, such as phone numbers, email addresses or mailing addresses.
  4. Financial Information : Data related to a person’s finances, including credit card numbers, bank account numbers, income records or financial transactions.
  5. Health and Medical Information : Information about a person’s health, medical history or healthcare treatments.
  6. Location Data : Data that can pinpoint a person’s geographical location, such as GPS coordinates or information derived from mobile devices.
  7. Online Identifiers : Information like IP addresses, cookies or other online tracking mechanisms that can be used to identify or track individuals online.
  8. Biometric Data : Unique physical or behavioural characteristics used for identification, such as fingerprints, facial recognition data or voiceprints.

  Sensitive Data

  Sensitive data is a special category of personal data prohibited from processing unless specific conditions are met, including users giving explicit consent. The data must also be necessary to fulfil one or more of a limited set of allowed purposes, such as reasons related to employment, social protections or legal claims.

  Sensitive information includes details about a person’s racial or ethnic origin, sexual orientation, political opinions, religion, trade union membership, biometric data or genetic data.

  What are the 7 main principles of GDPR ?

  The 7 principles of GDPR guide companies in how to properly handle personal data gathered from their users.

  

  The seven principles of GDPR are :

  1. Lawfulness, fairness and transparency

  Lawfulness means having legal grounds for data processing, such as consent, legitimate interests, contract and legal obligation. If you can achieve your objective without processing personal data, the basis is no longer lawful.

  Fairness means you’re processing data reasonably and in line with users’ best interests, and they wouldn’t be shocked if they find out what you’re using it for.

  Transparency means being open regarding when you’re processing user data, what you’re using it for and who you’re collecting it from.

  To get started with this, use our guide on creating a GDPR-compliant privacy policy.

  2. Purpose limitation

  You should only process user data for the original purposes you communicated to users when requesting their explicit consent. If you aim to undertake a new purpose, it must be compatible with the original stated purpose. Otherwise, you’ll need to ask for consent again.

  3. Data minimisation

  You should only collect as much data as you need to accomplish compliant objectives and nothing more, especially not other personally identifiable information (PII).

  Matomo provides several features for extensive data minimisation, including the ability to anonymize IP addresses.

  Data minimisation is well-liked by users. Around 70% of people have taken active steps towards protecting their identity online, so they’ll likely appreciate any principles that help them in this effort.

  

  4. Accuracy

  The user data you process should be accurate and up-to-date where necessary. You should have reasonable systems to catch inaccurate data and correct or delete it. If there are mistakes that you need to store, then you need to label them clearly as mistakes to keep them from being processed as accurate.

  5. Storage limitation

  This principle requires you to eliminate data you’re no longer using for the original purposes. You must implement time limits, after which you’ll delete or anonymize any user data on record. Matomo allows you to configure your system such that logs are automatically deleted after some time.

  6. Integrity and confidentiality

  This requires that data processors have security measures in place to protect data from threats such as hackers, loss and damage. As an open-source web analytics solution, Matomo enables you to verify its security first-hand.

  7. Accountability

  Accountability means you’re responsible for what you do with the data you collect. It’s your duty to maintain compliance and document everything for audits. Matomo tracks a lot of the data you’d need for this, including activity, task and application logs.

  Who does GDPR apply to ?

  The GDPR applies to any company that processes the personal data of EU citizens and residents (regardless of the location of the company). 

  If this is the first time you’ve heard about this, don’t worry ! Matomo provides tools that allow you to determine exactly what kinds of data you’re collecting and how they must be handled for full compliance. 

  Best practices for processing personal data under GDPR

  Companies subject to the GDPR need to be aware of several key principles and best practices to ensure they process personal data in a lawful and responsible manner.

  Here are some essential practices to implement :

  1. Lawful basis for processing : Organisations must have a lawful basis for processing personal data. Common lawful bases include the necessity of processing for compliance with a legal obligation, the performance of a contract, the protection of vital interests and tasks carried out in the public interest. Your organisation’s legitimate interests for processing must not override the individual’s legal rights. 
  2. Data minimisation : Collect and process only the personal data that is necessary for the specific purpose for which it was collected. Matomo’s anonymisation capabilities help you avoid collecting excessive or irrelevant data.
  3. Transparency : Provide clear and concise information to individuals about how their data will be processed. Privacy statements should be clear and accessible to users to allow them to easily understand how their data is used.
  4. Consent : If you are relying on consent as a lawful basis, make sure you design your privacy statements and consent forms to be usable. This lets you ensure that consent is freely given, specific, informed and unambiguous. Also, individuals must be able to withdraw their consent at any time.
  5. Data subject rights : You must have mechanisms in place to uphold the data subject’s individual rights, such as the rights to access, erase, rectify errors and restrict processing. Establish internal processes for handling such requests.
  6. Data protection impact assessments (DPIAs) : Conduct DPIAs for high-risk processing activities, especially when introducing new technologies or processing sensitive data.
  7. Security measures : You must implement appropriate technical security measures to maintain the safety of personal data. This can include ‌security tools such as encryption, firewalls and limited access controls, as well as organisational practices like regular security assessments. 
  8. Data breach response : Develop and maintain a data breach response plan. Notify relevant authorities and affected individuals of data breaches within the required timeframe.
  9. International data transfers : If transferring personal data outside the EU, ensure that appropriate safeguards are in place and consider GDPR provisions. These provisions allow data transfers from the EU to non-EU countries in three main ways :
     1. When the destination country has been deemed by the European Commission to have adequate data protection, making it similar to transferring data within the EU.
     2. Through the use of safeguards like binding corporate rules, approved contractual clauses or adherence to codes of conduct.
     3. In specific situations when none of the above apply, such as when an individual explicitly consents to the transfer after being informed of the associated risks.
  10. Data protection officers (DPOs) : Appoint a data protection officer if required by GDPR. DPOs are responsible for overseeing data protection compliance within the organisation.
  11. Privacy by design and default : Integrate data protection into the design of systems and processes. Default settings should prioritise user privacy, as is the case with something like Matomo’s first-party cookies.
  12. Documentation : Maintain records of data processing activities, including data protection policies, procedures and agreements. Matomo logs and backs up web server access, activity and more, providing a solid audit trail.
  13. Employee training : Employees who handle personal data must be properly trained to uphold data protection principles and GDPR compliance best practices. 
  14. Third-party contracts : If sharing data with third parties, have data processing agreements in place that outline the responsibilities and obligations of each party regarding data protection.
  15. Regular audits and assessments : Conduct periodic audits and assessments of data processing activities to ensure ongoing compliance. As mentioned previously, Matomo tracks and saves several key statistics and metrics that you’d need for a successful audit.
  16. Accountability : Demonstrate accountability by documenting and regularly reviewing compliance efforts. Be prepared to provide evidence of compliance to data protection authorities.
  17. Data protection impact on data analytics and marketing : Understand how GDPR impacts data analytics and marketing activities, including obtaining valid consent for marketing communications.

  Organisations should be on the lookout for GDPR updates, as the regulations may evolve over time. When in doubt, consult legal and privacy professionals to ensure compliance, as non-compliance could potentially result in significant fines, damage to reputation and legal consequences.

  What constitutes a GDPR breach ?

  Security incidents that compromise the confidentiality, integrity and/or availability of personal data are considered a breach under GDPR. This means a breach is not limited to leaks ; if you accidentally lose or delete personal data, its availability is compromised, which is technically considered a breach.

  What are the penalty fines for GDPR non-compliance ?

  The penalty fines for GDPR non-compliance are up to €20 million or up to 4% of the company’s revenue from the previous fiscal year, whichever is higher. This makes it so that small companies can also get fined, no matter how low-profile the breach is.

  In 2022, for instance, a company found to have mishandled user data was fined €2,000, and the webmaster responsible was personally fined €150.

  Is Matomo GDPR compliant ?

  Matomo is fully GDPR compliant and can ensure you achieve compliance, too. Here’s how :

  • Data anonymization and IP anonymization
  • GDPR Manager that helps you identify gaps in your compliance and address them effectively
  • Users can opt-out of all tracking
  • First-party cookies by default
  • Users can view the data collected
  • Capabilities to delete visitor data when requested
  • You own your data and it is not used for any other purposes (like advertising)
  • Visitor logs and profiles can be disabled
  • Data is stored in the EU (Matomo Cloud) or in any country of your choice (Matomo On-Premise)

  Is there a GDPR in the US ?

  There is no GDPR-equivalent law that covers the US as a whole. That said, US-based companies processing data from persons in the EU still need to adhere to GDPR principles.

  While there isn’t a federal data protection law, several states have enacted their own. One notable example is the California Consumer Privacy Act (CCPA), which Matomo is fully compliant with.

  Ready for GDPR-compliant analytics ?

  The GDPR lays out a set of regulations and penalties that govern the collection and processing of personal data from EU citizens and residents. A breach under GDPR attracts a fine of either up to €20 million or 4% of the company’s revenue, and the penalty applies to companies of all sizes.

  Matomo is fully GDPR compliant and provides several features and advanced privacy settings to ensure you ‌are as well, without sacrificing the resources you need for effective analytics. If you’re ready to get started, sign up for a 21-day free trial of Matomo — no credit card required.

  Disclaimer
  We are not lawyers and don’t claim to be. The information provided here is to help give an introduction to GDPR. We encourage every business and website to take data privacy seriously and discuss these issues with your lawyer if you have any concerns.

• Visualizing Call Graphs Using Gephi

  1er septembre 2014, par Multimedia Mike — General

  When I was at university studying computer science, I took a basic chemistry course. During an accompanying lab, the teaching assistant chatted me up and asked about my major. He then said, “Computer science ? Well, that’s just typing stuff, right ?”

  My impulsive retort : “Sure, and chemistry is just about mixing together liquids and coming up with different colored liquids, as seen on the cover of my high school chemistry textbook, right ?”

  
  
  

  In fact, pure computer science has precious little to do with typing (as is joked in CS circles, computer science is about computers in the same way that astronomy is about telescopes). However, people who study computer science often pursue careers as programmers, or to put it in fancier professional language, software engineers.

  So, what’s a software engineer’s job ? Isn’t it just typing ? That’s where I’ve been going with this overly long setup. After thinking about it for long enough, I like to say that a software engineer’s trade is managing complexity.

  A few years ago, I discovered Gephi, an open source tool for graph and data visualization. It looked neat but I didn’t have much use for it at the time. Recently, however, I was trying to get a better handle on a large codebase. I.e., I was trying to manage the project’s complexity. And then I thought of Gephi again.

  Prior Work
  One way to get a grip on a large C codebase is to instrument it for profiling and extract details from the profiler. On Linux systems, this means compiling and linking the code using the -pg flag. After running the executable, there will be a gmon.out file which is post-processed using the gprof command.

  GNU software development tools have a reputation for being rather powerful and flexible, but also extremely raw. This first hit home when I was learning how to use the GNU tool for code coverage — gcov — and the way it outputs very raw data that you need to massage with other tools in order to get really useful intelligence.

  And so it is with gprof output. The output gives you a list of functions sorted by the amount of processing time spent in each. Then it gives you a flattened call tree. This is arranged as “during the profiled executions, function c was called by functions a and b and called functions d, e, and f ; function d was called by function c and called functions g and h”.

  How can this call tree data be represented in a more instructive manner that is easier to navigate ? My first impulse (and I don’t think I’m alone in this) is to convert the gprof call tree into a representation suitable for interpretation by Graphviz. Unfortunately, doing so tends to generate some enormous and unwieldy static images.

  Feeding gprof Data To Gephi
  I learned of Gephi a few years ago and recalled it when I developed an interest in gaining better perspective on a large base of alien C code. To understand what this codebase is doing for a particular use case, instrument it with gprof, gather execution data, and then study the code paths.

  How could I feed the gprof data into Gephi ? Gephi supports numerous graphing formats including an XML-based format named GEXF.

  Thus, the challenge becomes converting gprof output to GEXF.

  Which I did.

  Demonstration
  I have been absent from FFmpeg development for a long time, which is a pity because a lot of interesting development has occurred over the last 2-3 years after a troubling period of stagnation. I know that 2 big video codec developments have been HEVC (next in the line of MPEG codecs) and VP9 (heir to VP8’s throne). FFmpeg implements them both now.

  I decided I wanted to study the code flow of VP9. So I got the latest FFmpeg code from git and built it using the options "--extra-cflags=-pg --extra-ldflags=-pg". Annoyingly, I also needed to specify "--disable-asm" because gcc complains of some register allocation snafus when compiling inline ASM in profiling mode (and this is on x86_64). No matter ; ASM isn’t necessary for understanding overall code flow.

  After compiling, the binary ‘ffmpeg_g’ will have symbols and be instrumented for profiling. I grabbed a sample from this VP9 test vector set and went to work.

  ./ffmpeg_g -i vp90-2-00-quantizer-00.webm -f null /dev/null
  gprof ./ffmpeg_g > vp9decode.txt
  convert-gprof-to-gexf.py vp9decode.txt > /bigdisk/vp9decode.gexf
  

  Gephi loads vp9decode.gexf with no problem. Using Gephi, however, can be a bit challenging if one is not versed in any data exploration jargon. I recommend this Gephi getting starting guide in slide deck form. Here’s what the default graph looks like :

  
  
  

  Not very pretty or helpful. BTW, that beefy arrow running from mid-top to lower-right is the call from decode_coeffs_b -> iwht_iwht_4x4_add_c. There were 18774 from the former to the latter in this execution. Right now, the edge thicknesses correlate to number of calls between the nodes, which I’m not sure is the best representation.

  Following the tutorial slide deck, I at least learned how to enable the node labels (function symbols in this case) and apply a layout algorithm. The tutorial shows the force atlas layout. Here’s what the node neighborhood looks like for probing file type :

  
  
  

  Okay, so that’s not especially surprising– avprobe_input_format3 calls all of the *_probe functions in order to automatically determine input type. Let’s find that decode_coeffs_b function and see what its neighborhood looks like :

  
  
  

  That’s not very useful. Perhaps another algorithm might help. I select the Fruchterman–Reingold algorithm instead and get a slightly more coherent representation of the decoding node neighborhood :

  
  
  

  Further Work
  Obviously, I’m just getting started with this data exploration topic. One thing I would really appreciate in such a tool is the ability to interactively travel the graph since that’s what I’m really hoping to get out of this experiment– watching the code flows.

  Perhaps someone else can find better use cases for visualizing call graph data. Thus, I have published the source code for this tool at Github.