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• OCPA, FDBR and TDPSA – What you need to know about the US’s new privacy laws

  22 juillet 2024, par Daniel Crough

  On July 1, 2024, new privacy laws took effect in Florida, Oregon, and Texas. People in these states now have more control over their personal data, signaling a shift in privacy policy in the United States. Here’s what you need to know about these laws and how privacy-focused analytics can help your business stay compliant.

  Consumer rights are front and centre across all three laws

  The Florida Digital Bill of Rights (FDBR), Oregon Consumer Privacy Act (OCPA), and Texas Data Privacy and Security Act (TDPSA) grant consumers similar rights.

  Access : Consumers can access their personal data held by businesses.

  Correction : Consumers can correct inaccurate data.

  Deletion : Consumers may request data deletion.

  Opt-Out : Consumers can opt-out of the sale of their personal data and targeted advertising.

  Oregon Consumer Privacy Act (OCPA)

  The Oregon Consumer Privacy Act (OCPA), signed into law on June 23, 2023, and effective as of July 1, 2024, grants Oregonians new rights regarding their personal data and imposes obligations on businesses. Starting July 1, 2025, authorities will enforce provisions that require data protection assessments, and businesses must recognize universal opt-out mechanisms by January 1, 2026. In Oregon, the OCPA applies to business that :

  • Either conduct business in Oregon or offer products and services to Oregon residents

  • Control or process the personal data of 100,000 consumers or more, or

  • Control or process the data of 25,000 or more consumers while receiving over 25% of their gross revenues from selling personal data.

  Exemptions include public bodies like state and local governments, financial institutions, and insurers that operate under specific financial regulations. The law also excludes protected health information covered by HIPAA and other specific federal regulations.

  Business obligations

  Data Protection Assessments : Businesses must conduct data protection assessments for high-risk processing activities, such as those involving sensitive data or targeting children.

  Consent for Sensitive Data : Businesses must secure explicit consent before collecting, processing, or selling sensitive personal data, such as racial or ethnic origin, religious beliefs, health information, biometric data, and geolocation.

  Universal Opt-out : Starting January 1, 2025, businesses must acknowledge universal opt-out mechanisms, like the Global Privacy Control, that allow consumers to opt out of data collection and processing activities.

  Enforcement

  The Oregon Attorney General can issue fines up to $7,500 per violation. There is no private right of action.

  Unique characteristics of the OCPA

  The OCPA differs from other state privacy laws by requiring affirmative opt-in consent for processing sensitive and children’s data, and by including nonprofit organisations under its scope. It also requires global browser opt-out mechanisms starting in 2026.

  Florida Digital Bill of Rights (FDBR)

  The Florida Digital Bill of Rights (FDBR) became law on June 6, 2023, and it came into effect on July 1, 2024. This law targets businesses with substantial operations or revenues tied to digital activities and seeks to protect the personal data of Florida residents by granting them greater control over their information and imposing stricter obligations on businesses. It applies to entities that :

  • Conduct business in Florida or provide products or services targeting Florida residents,

  • Have annual global gross revenues exceeding $1 billion,

  • Receive 50% or more of their revenues from digital advertising or operate significant digital platforms such as app stores or smart speakers with virtual assistants.

  Exemptions include governmental entities, nonprofits, financial institutions covered by the Gramm-Leach-Bliley Act, and entities covered by HIPAA.

  Business obligations

  Data Security Measures : Companies are required to implement reasonable data security measures to protect personal data from unauthorised access and breaches.

  Handling Sensitive Data : Explicit consent is required for processing sensitive data, which includes information like racial or ethnic origin, religious beliefs, and biometric data.

  Non-Discrimination : Entities must ensure they do not discriminate against consumers who exercise their privacy rights.

  Data Minimisation : Businesses must collect only necessary data.

  Vendor Management : Businesses must ensure that their processors and vendors also comply with the FDBR, regarding the secure handling and processing of personal data.

  Enforcement

  The Florida Attorney General can impose fines of up to $50,000 per violation, with higher penalties for intentional breaches.

  Unique characteristics of the FDBR

  Unlike broader privacy laws such as the California Consumer Privacy Act (CCPA), which apply to a wider range of businesses based on lower revenue thresholds and the volume of data processed, the FDBR distinguishes itself by targeting large-scale businesses with substantial revenues from digital advertising. The FDBR also emphasises specific consumer rights related to modern digital interactions, reflecting the evolving landscape of online privacy concerns.

  Texas Data Privacy and Security Act (TDPSA)

  The Texas Data Privacy and Security Act (TDPSA), signed into law on June 16, 2023, and effective as of July 1, 2024, enhances data protection for Texas residents. The TDPSA applies to entities that :

  • Conduct business in Texas or offer products or services to Texas residents.

  • Engage in processing or selling personal data.

  • Do not fall under the classification of small businesses according to the U.S. Small Business Administration’s criteria, which usually involve employee numbers or average annual receipts. 

  The law excludes state agencies, political subdivisions, financial institutions compliant with the Gramm-Leach-Bliley Act, and entities compliant with HIPAA.

  Business obligations

  Data Protection Assessments : Businesses must conduct data protection assessments for processing activities that pose a heightened risk of harm to consumers, such as processing for targeted advertising, selling personal data, or profiling.

  Consent for Sensitive Data : Businesses must get explicit consent before collecting, processing, or selling sensitive personal data, such as racial or ethnic origin, religious beliefs, health information, biometric data, and geolocation.

  Companies must have adequate data security practices based on the personal information they handle.

  Data Subject Access Requests (DSARs) : Businesses must respond to consumer requests regarding their personal data (e.g., access, correction, deletion) without undue delay, but no later than 45 days after receipt of the request.

  Sale of Data : If businesses sell personal data, they must disclose these practices to consumers and provide them with an option to opt out.

  Universal Opt-Out Compliance : Starting January 1, 2025, businesses must recognise universal opt-out mechanisms like the Global Privacy Control, enabling consumers to opt out of data collection and processing activities.

  Enforcement

  The Texas Attorney General can impose fines up to $25,000 per violation. There is no private right of action.

  Unique characteristics of the TDPSA

  The TDPSA stands out for its small business carve-out, lack of specific thresholds based on revenue or data volume, and requirements for recognising universal opt-out mechanisms starting in 2025. It also mandates consent for processing sensitive data and includes specific measures for data protection assessments and privacy notices.

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  Privacy notices across Florida, Oregon, and Texas

  All three laws include a mandate for privacy notices, though there are subtle variations in their specific requirements. Here’s a breakdown of these differences :

  FDBR privacy notice requirements

  Clarity : Privacy notices must clearly explain the collection and use of personal data.

  Disclosure : Notices must inform consumers about their rights, including the right to access, correct, delete their data, and opt-out of data sales and targeted advertising.

  Specificity : Businesses must disclose if they sell personal data or use it for targeted advertising.

  Security Practices : The notice should describe the data security measures in place.

  OCPA privacy notice requirements

  Comprehensive Information : Notices must provide information about the personal data collected, the purposes for processing, and any third parties that can access it.

  Consumer Rights : Must plainly outline consumers’ rights to access, correct, delete their data, and opt-out of data sales, targeted advertising, and profiling.

  Sensitive Data : To process sensitive data, businesses or entities must get explicit consent and communicate it.

  Universal Opt-Out : Starting January 1, 2026, businesses must recognise and honour universal opt-out mechanisms.

  TDPSA privacy notice requirements

  Detailed Notices : Must provide clear and detailed information about data collection practices, including the data collected and the purposes for its use.

  Consumer Rights : Must inform consumers of their rights to access, correct, delete their data, and opt-out of data sales and targeted advertising.

  High-Risk Processing : Notices should include information about any high-risk processing activities and the safeguards in place.

  Sensitive Data : To process sensitive data, entities and businesses must get explicit consent.

  What these laws mean for your businesses

  Businesses operating in Florida, Oregon, and Texas must now comply with these new data privacy laws. Here’s what you can do to avoid fines :

  1. Understand the Laws : Familiarise yourself with the specific requirements of the FDBR, OCPA, and TDPSA, including consumer rights and business obligations.

  2. Implement Data Protection Measures : Ensure you have robust data security measures in place. This includes conducting regular data protection assessments, especially for high-risk processing activities.

  3. Update Privacy Policies : Provide clear and comprehensive privacy notices that inform consumers about their rights and how their data is processed.

  4. Obtain Explicit Consent : For sensitive data, make sure you get explicit consent from consumers. This includes information like health, race, sexual orientation, and more.

  5. Manage Requests Efficiently : Be prepared to handle requests from consumers to access, correct, delete their data, and opt-out of data sales and targeted advertising within the stipulated timeframes.

  6. Recognise Opt-Out Mechanisms : For Oregon, businesses must be ready to implement and recognise universal opt-out mechanisms by January 1, 2026. In Texas, opt-out enforcement begins in 2026. In Florida, the specific opt-out provisions began on July 1, 2024.

  7. Stay Updated : Keep abreast of any changes or updates to these laws to ensure ongoing compliance. Keep an eye on the Matomo blog or sign up for our newsletter to stay in the know.

  Are we headed towards a more privacy-focused future in the United States ?

  Florida, Oregon, and Texas are joining states like California, Virginia, Colorado, Connecticut, Utah, Iowa, Indiana, Tennessee, and Montana in strengthening consumer privacy protections. This trend could signify a shift in US policy towards a more privacy-focused internet, underlining the importance of consumer data rights and transparent business practices. Even if these laws do not apply to your business, considering updates to your data and privacy policies is wise. Fortunately, there are tools and solutions designed for privacy and compliance to help you navigate these changes.

  Avoid fines and get better data with Matomo

  Most analytics tools don’t prioritize safeguarding user data. At Matomo, we believe everyone has the right to data sovereignty, privacy and amazing analytics. Matomo offers a solution that meets privacy regulations while delivering incredible insights. With Matomo, you get :

  100% Data Ownership : Keep full control over your data, ensuring it is used according to your privacy policies.

  Privacy Protection : Built with privacy in mind, Matomo helps businesses comply with privacy laws.

  Powerful Features : Gain insights with tools like heatmaps, session recordings, and A/B testing.

  Open Source : Matomo’s is open-source and committed to transparency and customisation.

  Flexibility : Choose to host Matomo on your servers or in the cloud for added security.

  No Data Sampling : Ensure accurate and complete insights without data sampling.

  Privacy Compliance : Easily meet GDPR and other requirements, with data stored securely and never sold or shared.

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  Disclaimer : This content is provided for informational purposes only and is not intended as legal advice. While we strive to ensure the accuracy and timeliness of the information provided, the laws and regulations surrounding privacy are complex and subject to change. We recommend consulting with a qualified legal professional to address specific legal issues related to your circumstances. 

• Why when converting avi video file to another format the first 2-3 seconds are blurry ?

  13 juin 2016, par Sharon Gabriel

  The source file is avi. The target new file is mp4.
  The first 2-3 seconds are blurry. Then after 2-3 second the whole video until the end is smooth and sharp.

  Another sub question is how come that 2.16 GB avi file after conversion using ffmpeg is only 1.34 MB ? It’s not part of a movie or something it’s collection of screenshots images i did in c# and then used AviFile Lib to create from them a avi video file. and yet from 2.16 GB to 1.34 MB and it keep the quality i think almost the same quality like the original avi file and the same duration 2:20 minutes.

  About the blurry problem this is my code where i set the ffmpeg arguments and set the process :

  private void Convert()
  
          {
  
              try
  
              {
  
                  Control.CheckForIllegalCrossThreadCalls = false;
  
                  if (ComboBox1.SelectedIndex == 3) 
  
                  {
  
                      strFFCMD = " -i " + (char)34 + InputFile + (char)34 + " -c:v libx264 -s 1920x1080 -pix_fmt yuv420p -qp 18 -profile high444 -c:a libvo_aacenc -b:a 128k -ar 44100 -ac 2 -y " + OutputFile;
  
                  }    
  
                  psiProcInfo.FileName = exepath;
  
                  psiProcInfo.Arguments = strFFCMD;        
  
                  psiProcInfo.UseShellExecute = false;      
  
                  psiProcInfo.WindowStyle = ProcessWindowStyle.Hidden;    
  
                  psiProcInfo.RedirectStandardError = true;             
  
                  psiProcInfo.RedirectStandardOutput = true;         
  
                  psiProcInfo.CreateNoWindow = true;                 
  
                  prcFFMPEG.StartInfo = psiProcInfo;           
  
                  prcFFMPEG.Start();
  
                  ffReader = prcFFMPEG.StandardError;
  
  
  
                  do
  
                  {
  
                      if (Bgw1.CancellationPending)
  
                      {
  
                          return;
  
                      }
  
                      Button5.Enabled = true;
  
                      Button3.Enabled = false;
  
                      strFFOUT = ffReader.ReadLine();                    
  
                      RichTextBox1.Text = strFFOUT;
  
                      if (strFFOUT != null)
  
                      {
  
                          if (strFFOUT.Contains("frame="))
  
                          {
  
                              currentFramestr = strFFOUT.Substring(7, 6).Trim();
  
                              Regex rx = new Regex(@"^\d+");
  
                              Match m = rx.Match(currentFramestr);
  
                              if (m.Success)
  
                              {
  
                                  currentFrameInt = System.Convert.ToInt32(m.Value);
  
                              }
  
                          }
  
                      }
  
                      string percentage = ((double)ProgressBar1.Value / (double)ProgressBar1.Maximum * 100.0).ToString();
  
                      textBox3.Text = ProgressBar1.Value.ToString();                    
  
                      ProgressBar1.Maximum = FCount + 1;
  
                      ProgressBar1.Value = (currentFrameInt);
  
                      Label12.Text = "Current Encoded Frame: " + currentFrameInt;
  
                      Label11.Text = percentage;
  
                  } while (!(prcFFMPEG.HasExited || string.IsNullOrEmpty(strFFOUT)));
  
              }
  
              catch(Exception err)
  
              {
  
                  string errors = err.ToString();
  
              }
  
          }

  psiProcInfo is ProcessStartInfo

  prcFFMPEG is Process

  And this is how it looks like when i play the target the new created converted video file the mp4 the first seconds :

  Duration : 00:02:20

  Width : 1920 Height : 1080

  Data Rate and Total Rate both : 80kbps

  Frame rate : 2 frames/second

  

  This is the output of the ffmpeg console while converting the file.

    ffmpeg version 2.8.git Copyright (c) 2000-2015 the FFmpeg developers
  
    built with gcc 5.2.0 (GCC)
  
    configuration: --enable-gpl --enable-version3 --disable-w32threads --enable-avisynth --enable-bzlib --enable-fontconfig --enable-frei0r --enable-gnutls --enable-iconv --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libdcadec --enable-libfreetype --enable-libgme --enable-libgsm --enable-libilbc --enable-libmodplug --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libopus --enable-librtmp --enable-libschroedinger --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvo-aacenc --enable-libvo-amrwbenc --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs --enable-libxvid --enable-libzimg --enable-lzma --enable-decklink --enable-zlib
  
    libavutil      55. 11.100 / 55. 11.100
  
    libavcodec     57. 17.100 / 57. 17.100
  
    libavformat    57. 20.100 / 57. 20.100
  
    libavdevice    57.  0.100 / 57.  0.100
  
    libavfilter     6. 21.100 /  6. 21.100
  
    libswscale      4.  0.100 /  4.  0.100
  
    libswresample   2.  0.101 /  2.  0.101
  
    libpostproc    54.  0.100 / 54.  0.100
  
  [avi @ 00000147a882b660] Stream #0: not enough frames to estimate rate; consider increasing probesize
  
  Input #0, avi, from 'C:\temp\video\new.avi':
  
    Duration: 00:02:20.50, start: 0.000000, bitrate: 132710 kb/s
  
      Stream #0:0: Video: rawvideo, bgra, 1920x1080, 2 fps, 2 tbr, 2 tbn, 2 tbc
  
  Please use -profile:a or -profile:v, -profile is ambiguous
  
  Codec AVOption b (set bitrate (in bits/s)) specified for output file #0 (C:\temp\video\5.mp4) has not been used for any stream. The most likely reason is either wrong type (e.g. a video option with no video streams) or that it is a private option of some encoder which was not actually used for any stream.
  
  [libx264 @ 00000147a882c820] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2
  
  [libx264 @ 00000147a882c820] profile High, level 4.0
  
  [libx264 @ 00000147a882c820] 264 - core 148 r2638 7599210 - H.264/MPEG-4 AVC codec - Copyleft 2003-2015 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=2 scenecut=40 intra_refresh=0 rc=cqp mbtree=0 qp=18 ip_ratio=1.40 pb_ratio=1.30 aq=0
  
  Output #0, mp4, to 'C:\temp\video\5.mp4':
  
    Metadata:
  
      encoder         : Lavf57.20.100
  
      Stream #0:0: Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv420p, 1920x1080, q=-1--1, 2 fps, 16384 tbn, 2 tbc
  
      Metadata:
  
        encoder         : Lavc57.17.100 libx264
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))
  
  Press [q] to stop, [?] for help
  
  frame=    8 fps=0.0 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
  
  frame=   15 fps= 14 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
  
  frame=   21 fps= 13 q=18.0 size=      92kB time=00:00:00.00 bitrate=N/A speed=   0x    
  
  frame=   30 fps= 14 q=18.0 size=     141kB time=00:00:04.50 bitrate= 257.3kbits/s speed=2.03x    
  
  frame=   37 fps= 13 q=20.0 size=     164kB time=00:00:08.00 bitrate= 167.6kbits/s speed=2.82x    
  
  frame=   46 fps= 14 q=18.0 size=     185kB time=00:00:12.50 bitrate= 121.0kbits/s speed= 3.7x    
  
  frame=   51 fps= 13 q=19.0 size=     194kB time=00:00:15.00 bitrate= 106.1kbits/s speed=3.87x    
  
  frame=   58 fps= 13 q=18.0 size=     210kB time=00:00:18.50 bitrate=  93.2kbits/s speed=4.19x    
  
  frame=   65 fps= 13 q=20.0 size=     224kB time=00:00:22.00 bitrate=  83.6kbits/s speed=4.46x    
  
  frame=   71 fps= 13 q=19.0 size=     238kB time=00:00:25.00 bitrate=  78.1kbits/s speed=4.56x    
  
  frame=   78 fps= 13 q=18.0 size=     253kB time=00:00:28.50 bitrate=  72.6kbits/s speed=4.75x    
  
  frame=   83 fps= 13 q=19.0 size=     265kB time=00:00:31.00 bitrate=  70.0kbits/s speed= 4.7x    
  
  frame=   89 fps= 12 q=20.0 size=     280kB time=00:00:34.00 bitrate=  67.4kbits/s speed=4.73x    
  
  frame=   95 fps= 12 q=19.0 size=     291kB time=00:00:37.00 bitrate=  64.5kbits/s speed=4.73x    
  
  frame=  102 fps= 12 q=18.0 size=     308kB time=00:00:40.50 bitrate=  62.3kbits/s speed=4.84x    
  
  frame=  107 fps= 12 q=19.0 size=     317kB time=00:00:43.00 bitrate=  60.4kbits/s speed=4.82x    
  
  frame=  115 fps= 12 q=19.0 size=     336kB time=00:00:47.00 bitrate=  58.6kbits/s speed=4.96x    
  
  frame=  123 fps= 12 q=20.0 size=     354kB time=00:00:51.00 bitrate=  56.8kbits/s speed=5.09x    
  
  frame=  132 fps= 12 q=20.0 size=     371kB time=00:00:55.50 bitrate=  54.8kbits/s speed=5.25x    
  
  frame=  139 fps= 13 q=20.0 size=     392kB time=00:00:59.00 bitrate=  54.5kbits/s speed=5.32x    
  
  frame=  146 fps= 13 q=19.0 size=     408kB time=00:01:02.50 bitrate=  53.5kbits/s speed=5.37x    
  
  frame=  150 fps= 12 q=20.0 size=     417kB time=00:01:04.50 bitrate=  52.9kbits/s speed=5.28x    
  
  frame=  155 fps= 12 q=18.0 size=     428kB time=00:01:07.00 bitrate=  52.4kbits/s speed=5.25x    
  
  frame=  161 fps= 12 q=20.0 size=     441kB time=00:01:10.00 bitrate=  51.6kbits/s speed=5.26x    
  
  frame=  167 fps= 12 q=19.0 size=     462kB time=00:01:13.00 bitrate=  51.9kbits/s speed=5.29x    
  
  frame=  174 fps= 12 q=20.0 size=     483kB time=00:01:16.50 bitrate=  51.7kbits/s speed=5.33x    
  
  frame=  181 fps= 12 q=18.0 size=     614kB time=00:01:20.00 bitrate=  62.8kbits/s speed=5.36x    
  
  frame=  187 fps= 12 q=20.0 size=     763kB time=00:01:23.00 bitrate=  75.3kbits/s speed=5.35x    
  
  frame=  193 fps= 12 q=19.0 size=     852kB time=00:01:26.00 bitrate=  81.2kbits/s speed=5.36x    
  
  frame=  199 fps= 12 q=18.0 size=     865kB time=00:01:29.00 bitrate=  79.6kbits/s speed=5.37x    
  
  frame=  206 fps= 12 q=20.0 size=     932kB time=00:01:32.50 bitrate=  82.6kbits/s speed=5.39x    
  
  frame=  211 fps= 12 q=20.0 size=     943kB time=00:01:35.00 bitrate=  81.3kbits/s speed=5.38x    
  
  frame=  217 fps= 12 q=18.0 size=    1007kB time=00:01:38.00 bitrate=  84.1kbits/s speed=5.38x    
  
  frame=  223 fps= 12 q=20.0 size=    1175kB time=00:01:41.00 bitrate=  95.3kbits/s speed=5.38x    
  
  frame=  230 fps= 12 q=20.0 size=    1195kB time=00:01:44.50 bitrate=  93.7kbits/s speed=5.42x    
  
  frame=  235 fps= 12 q=18.0 size=    1205kB time=00:01:47.00 bitrate=  92.3kbits/s speed= 5.4x    
  
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• Privacy-enhancing technologies : Balancing data utility and security

  18 juillet, par Joe

  In the third quarter of 2024, data breaches exposed 422.61 million records, affecting millions of people around the world. This highlights the need for organisations to prioritise user privacy. 

  Privacy-enhancing technologies can help achieve this by protecting sensitive information and enabling safe data sharing. 

  This post explores privacy-enhancing technologies, including their types, benefits, and how our website analytics platform, Matomo, supports them by providing privacy-focused features.

  What are privacy-enhancing technologies ? 

  Privacy Enhancing Technologies (PETs) are tools that protect personal data while allowing organisations to process information responsibly. 

  In industries like healthcare, finance and marketing, businesses often need detailed analytics to improve operations and target audiences effectively. However, collecting and processing personal data can lead to privacy concerns, regulatory challenges, and reputational risks.

  PETs minimise the collection of sensitive information, enhance security and allow users to control how companies use their data. 

  Global privacy laws like the following are making PETs essential for compliance :

  • General Data Protection Regulation (GDPR) in the European Union
  • California Consumer Privacy Act (CCPA) in California
  • Personal Information Protection and Electronic Documents Act (PIPEDA) in Canada
  • Lei Geral de Proteção de Dados (LGPD) in Brazil

  Non-compliance can lead to severe penalties, including hefty fines and reputational damage. For example, under GDPR, organisations may face fines of up to €20 million or 4% of their global annual revenue for serious violations. 

  Types of PETs 

  What are the different types of technologies available for privacy protection ? Let’s take a look at some of them. 

  Homomorphic encryption

  Homomorphic encryption is a cryptographic technique in which users can perform calculations on cipher text without decrypting it first. When the results are decrypted, they match those of the same calculation on plain text. 

  This technique keeps data safe during processing, and users can analyse data without exposing private or personal data. It is most useful in financial services, where analysts need to protect sensitive customer data and secure transactions. 

  Despite these advantages, homomorphic encryption can be complex to compute and take longer than other traditional methods. 

  Secure Multi-Party Computation (SMPC)

  SMPC enables joint computations on private data without revealing the raw data. 

  In 2021, the European Data Protection Board (EDPB) issued technical guidance supporting SMPC as a technology that protects privacy requirements. This highlights the importance of SMPC in healthcare and cybersecurity, where data sharing is necessary but sensitive information must be kept safe. 

  For example, several hospitals can collaborate on research without sharing patient records. They use SMPC to analyse combined data while keeping individual records confidential. 

  Synthetic data

  Synthetic data is artificially generated to mimic real datasets without revealing actual information. It is useful for training models without compromising privacy. 

  Imagine a hospital wants to train an AI model to predict patient outcomes based on medical records. Sharing real patient data, however, poses privacy challenges, so that can be changed with synthetic data. 

  Synthetic data may fail to capture subtle nuances or anomalies in real-world datasets, leading to inaccuracies in AI model predictions.

  Pseudonymisation

  Pseudonymisation replaces personal details with fake names or codes, making it hard to determine who the information belongs to. This helps keep people’s personal information safe. Even if someone gets hold of the data, it’s not easy to connect it back to real individuals. 

  

  Pseudonymisation works differently from synthetic data, though both help protect individual privacy. 

  When we pseudonymise, we take factual information and replace the bits that could identify someone with made-up labels. Synthetic data takes an entirely different approach. It creates new, artificial information that looks and behaves like real data but doesn’t contain any details about real people.

  Differential privacy

  Differential privacy adds random noise to datasets. This noise helps protect individual entries while still allowing for overall analysis of the data. 

  It’s useful in statistical studies where trends need to be understood without accessing personal details.

  For example, imagine a survey about how many hours people watch TV each week. 

  Differential privacy would add random variation to each person’s answer, so users couldn’t tell exactly how long John or Jane watched TV. 

  However, they could still see the average number of hours everyone in the group watched, which helps researchers understand viewing habits without invading anyone’s privacy.

  Zero-Knowledge Proofs (ZKP)

  Zero-knowledge proofs help verify the truth without exposing sensitive details. This cryptographic approach lets someone prove they know something or meet certain conditions without revealing the actual information behind that proof.

  Take ZCash as a real-world example. While Bitcoin publicly displays every financial transaction detail, ZCash offers privacy through specialised proofs called Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs). These mathematical proofs confirm that a transaction follows all the rules without broadcasting who sent money, who received it, or how much changed hands.

  The technology comes with trade-offs, though. 

  Creating and checking these proofs demands substantial computing power, which slows down transactions and drives up costs. Implementing these systems requires deep expertise in advanced cryptography, which keeps many organisations from adopting them despite their benefits.

  Trusted Execution Environment (TEE)

  TEEs create special protected zones inside computer processors where sensitive code runs safely. These secure areas process valuable data while keeping it away from anyone who shouldn’t see it.

  TEEs are widely used in high-security applications, such as mobile payments, digital rights management (DRM), and cloud computing.

  Consider how companies use TEEs in the cloud : A business can run encrypted datasets within a protected area on Microsoft Azure or AWS Nitro Enclaves. Due to this setup, even the cloud provider can’t access the private data or see how the business uses it. 

  TEEs do face limitations. Their isolated design makes them struggle with large or spread-out computing tasks, so they don’t work well for complex calculations across multiple systems.

  Different TEE implementations often lack standardisation, so there can be compatibility issues and dependence on specific vendors. If the vendor stops the product or someone discovers a security flaw, switching to a new solution often proves expensive and complicated.

  Obfuscation (Data masking)

  Data masking involves replacing or obscuring sensitive data to prevent unauthorised access. 

  It replaces sensitive data with fictitious but realistic values. For example, a customer’s credit card number might be masked as “1234-XXXX-XXXX-5678.” 

  The original data is permanently altered or hidden, and the masked data can’t be reversed to reveal the original values.

  Federated learning

  Federated learning is a machine learning approach that trains algorithms across multiple devices without centralising the data. This method allows organisations to leverage insights from distributed data sources while maintaining user privacy.

  For example, NVIDIA’s Clara platform uses federated learning to train AI models for medical imaging (e.g., detecting tumours in MRI scans). 

  Hospitals worldwide contribute model updates from their local datasets to build a global model without sharing patient scans. This approach may be used to classify stroke types and improve cancer diagnosis accuracy.

  Now that we have explored the various types of PETs, it’s essential to understand the principles that guide their development and use. 

  Key principles of PET (+ How to enable them with Matomo) 

  PETs are based on several core principles that aim to balance data utility with privacy protection. These principles include :

  Data minimisation

  Data minimisation is a core PET principle focusing on collecting and retaining only essential data.

  Matomo, an open-source web analytics platform, helps organisations to gather insights about their website traffic and user behaviour while prioritising privacy and data protection. 

  Recognising the importance of data minimisation, Matomo offers several features that actively support this principle :

  • Cookieless tracking : Eliminates reliance on cookies, reducing unnecessary data collection.
  • IP anonymisation : Automatically anonymises IP addresses, preventing identification of individual users.

  

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  • Custom data retention policies : Allows organisations to define how long user data is stored before automatic deletion.

  7Assets, a fintech company, was using Google Analytics and Plausible as their web analytics tools. 

  However, with Google Analytics, they faced a problem of unnecessary data tracking, which created legal work overhead. Plausible didn’t have the features for the kind of analysis they wanted. 

  They switched to Matomo to enjoy the balance of privacy yet detailed analytics. With Matomo, they had full control over their data collection while also aligning with privacy and compliance requirements.

  Transparency and User Control

  Transparency and user control are important for trust and compliance. 

  Matomo enables these principles through :

  • Consent management : Offers integration with Consent Mangers (CMPs), like Cookiebot and Osano, for collecting and managing user consent.
  • Respect for DoNotTrack settings : Honours browser-based privacy preferences by default, empowering users with control over their data.

  

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  • Opt-out mechanisms : These include iframe features that allow visitors to opt out of tracking. 

  Security and Confidentiality

  Security and confidentiality protect sensitive data against inappropriate access. 

  Matomo achieves this through :

  • On-premise hosting : Gives organisations the ability to host analytics data on-site for complete data control.
  • Data security : Protects stored information through access controls, audit logs, two-factor authentication and SSL encryption.
  • Open source code : Enables community reviews for better security and transparency.

  Purpose Limitation

  Purpose limitation means organisations use data solely for the intended purpose and don’t share or sell it to third parties. 

  Matomo adheres to this principle by using first-party cookies by default, so there’s no third-party involvement. Matomo offers 100% data ownership, meaning all the data organisations get from our web analytics is of the organisation, and we don’t sell it to any external parties. 

  Compliance with Privacy Regulations

  Matomo aligns with global privacy laws such as GDPR, CCPA, HIPAA, LGPD and PECR. Its compliance features include :

  • Configurable data protection : Matomo can be configured to avoid tracking personally identifiable information (PII).
  • Data subject request tools : These provide mechanisms for handling requests like data deletion or access in accordance with legal frameworks.
  • GDPR manager : Matomo provides a GDPR Manager that helps businesses manage compliance by offering features like visitor log deletion and audit trails to support accountability.

  

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  Mandarine Academy is a French-based e-learning company. It found that complying with GDPR regulations was difficult with Google Analytics and thought GA4 was hard to use. Therefore, it was searching for a web analytics solution that could help it get detailed feedback on its site’s strengths and friction points while respecting privacy and GDPR compliance. With Matomo, it checked all the boxes.

  Data collaboration : A key use case of PETs

  One specific area where PETs are quite useful is data collaboration. Data collaboration is important for organisations for research and innovation. However, data privacy is at stake. 

  This is where tools like data clean rooms and walled gardens play a significant role. These use one or more types of PETs (they aren’t PETs themselves) to allow for secure data analysis. 

  Walled gardens restrict data access but allow analysis within their platforms. Data clean rooms provide a secure space for data analysis without sharing raw data, often using PETs like encryption. 

  Tackling privacy issues with PETs 

  Amidst data breaches and privacy concerns, organisations must find ways to protect sensitive information while still getting useful insights from their data. Using PETs is a key step in solving these problems as they help protect data and build customer trust. 

  Tools like Matomo help organisations comply with privacy laws while keeping data secure. They also allow individuals to have more control over their personal information, which is why 1 million websites use Matomo.

  In addition to all the nice features, switching to Matomo is easy :

  “We just followed the help guides, and the setup was simple,” said Rob Jones. “When we needed help improving our reporting, the support team responded quickly and solved everything in one step.” 

  To experience Matomo, sign up for our 21-day free trial, no credit card details needed.