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• Error of "Built target opencv_imgproc" while compiling opencv2

  23 mars 2017, par Hong

  Following https://github.com/menpo/conda-opencv3, while I compile opencv, there is following error (please read the error at the end of the post). The only change I made is to enable ffmpeg by modifying "-DWITH_FFMPEG=1" in BUILD.SH. Any suggestion ?

  $conda build conda/
  
  BUILD START: opencv3-3.1.0-py27_0
  
  updating index in: /home/cocadas/anaconda2/conda-bld/linux-64
  
  updating index in: /home/cocadas/anaconda2/conda-bld/noarch
  
  
  
  The following NEW packages will be INSTALLED:
  
  
  
  bzip2:      1.0.6-3            
  
  cmake:      3.6.3-0            
  
  curl:       7.52.1-0           
  
  eigen:      3.2.7-0       menpo
  
  expat:      2.1.0-0            
  
  mkl:        2017.0.1-0         
  
  ncurses:    5.9-10             
  
  numpy:      1.12.1-py27_0      
  
  openssl:    1.0.2k-1           
  
  pip:        9.0.1-py27_1       
  
  python:     2.7.13-0           
  
  readline:   6.2-2              
  
  setuptools: 27.2.0-py27_0      
  
  sqlite:     3.13.0-0           
  
  tk:         8.5.18-0           
  
  wheel:      0.29.0-py27_0      
  
  xz:         5.2.2-1            
  
  zlib:       1.2.8-3            
  
  Source cache directory is: /home/cocadas/anaconda2/conda-bld/src_cache
  
  Found source in cache: opencv-3.1.0.tar.gz
  
  Extracting download
  
  Applying patch: u'/home/cocadas/conda-opencv3/conda/no_rpath.patch'
  
  patching file CMakeLists.txt
  
  patch unexpectedly ends in middle of line
  
  Hunk #1 succeeded at 397 with fuzz 1 (offset 11 lines).
  
  Package: opencv3-3.1.0-py27_0
  
  source tree in: /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0
  
  
  
  source /home/cocadas/anaconda2/bin/activate /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/_b_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pl
  
  mkdir build
  
  cd build
  
  CMAKE_GENERATOR='Unix Makefiles'
  
  CMAKE_ARCH=-m64
  
  ++ uname -s
  
  SHORT_OS_STR=Linux
  
  '[' Linux == Linux ']'
  
  DYNAMIC_EXT=so
  
  TBB=
  
  OPENMP=-DWITH_OPENMP=1
  
  IS_OSX=0
  
  
  
  -- 3rdparty dependencies: zlib libjpeg libwebp libpng libtiff libjasper IlmImf
  
  --
  
  -- OpenCV modules:
  
  -- To be built: core flann hdf imgproc ml photo reg surface_matching video dnn fuzzy imgcodecs shape videoio highgui objdetect plot superres xobjdetect xphoto bgsegm bioinspired dpm face features2d line_descriptor saliency text calib3d ccalib datasets rgbd stereo structured_light tracking videostab xfeatures2d ximgproc aruco optflow sfm stitching python2
  
  -- Disabled: world contrib_world
  
  -- Disabled by dependency: -
  
  -- Unavailable: cudaarithm cudabgsegm cudacodec cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev java python3 ts viz cvv matlab
  
  --
  
  -- GUI:
  
  -- QT: NO
  
  -- GTK+ 3.x: YES (ver 3.18.9)
  
  -- GThread : YES (ver 2.48.2)
  
  -- GtkGlExt: NO
  
  -- OpenGL support: NO
  
  -- VTK support: NO
  
  --
  
  -- Media I/O:
  
  -- ZLib: build (ver 1.2.8)
  
  -- JPEG: build (ver 90)
  
  -- WEBP: build (ver 0.3.1)
  
  -- PNG: build (ver 1.6.19)
  
  -- TIFF: build (ver 42 - 4.0.2)
  
  -- JPEG 2000: build (ver 1.900.1)
  
  -- OpenEXR: build (ver 1.7.1)
  
  -- GDAL: NO
  
  --
  
  -- Video I/O:
  
  -- DC1394 1.x: NO
  
  -- DC1394 2.x: YES (ver 2.2.4)
  
  -- FFMPEG: YES
  
  -- codec: YES (ver 56.60.100)
  
  -- format: YES (ver 56.40.101)
  
  -- util: YES (ver 54.31.100)
  
  -- swscale: YES (ver 3.1.101)
  
  -- resample: NO
  
  -- gentoo-style: YES
  
  -- GStreamer: NO
  
  -- OpenNI: NO
  
  -- OpenNI PrimeSensor Modules: NO
  
  -- OpenNI2: NO
  
  -- PvAPI: NO
  
  -- GigEVisionSDK: NO
  
  -- UniCap: NO
  
  -- UniCap ucil: NO
  
  -- V4L/V4L2: Using libv4l1 (ver 1.10.0) / libv4l2 (ver 1.10.0)
  
  -- XIMEA: NO
  
  -- Xine: NO
  
  -- gPhoto2: NO
  
  --
  
  -- Parallel framework: OpenMP
  
  --
  
  -- Other third-party libraries:
  
  -- Use IPP: 9.0.1 [9.0.1]
  
  -- at: /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/3rdparty/ippicv/unpack/ippicv_lnx
  
  -- Use IPP Async: NO
  
  -- Use VA: NO
  
  -- Use Intel VA-API/OpenCL: NO
  
  -- Use Eigen: YES (ver 3.2.7)
  
  -- Use Cuda: NO
  
  -- Use OpenCL: NO
  
  -- Use custom HAL: NO
  
  --
  
  -- Python 2:
  
  
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:554:22: error: ‘H5Tclose’ was not declared in this scope
  
  H5Tclose( dstype );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:555:22: error: ‘H5Sclose’ was not declared in this scope
  
  H5Sclose( dspace );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:557:22: error: ‘H5Dclose’ was not declared in this scope
  
  H5Dclose( dsdata );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp: At global scope:
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:466:50: warning: unused parameter ‘dslabel’ [-Wunused-parameter]
  
  void HDF5Impl::dsread( OutputArray Array, String dslabel,
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp: In member function ‘virtual void cv::hdf::HDF5Impl::dswrite(cv::InputArray, cv::String, const int*, const int*) const’:
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:583:5: error: ‘hsize_t’ was not declared in this scope
  
  hsize_t dsdims[n_dims];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:584:13: error: expected ‘;’ before ‘offset’
  
  hsize_t offset[n_dims];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:588:7: error: ‘offset’ was not declared in this scope
  
  offset[d] = 0;
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:590:7: error: ‘dsdims’ was not declared in this scope
  
  dsdims[d] = matrix.size[d];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:601:9: error: ‘dsdims’ was not declared in this scope
  
  dsdims[d] = dims_counts[d];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:605:5: error: ‘hid_t’ was not declared in this scope
  
  hid_t dsdata = H5Dopen( m_h5_file_id, dslabel.c_str(), H5P_DEFAULT );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:608:11: error: expected ‘;’ before ‘dspace’
  
  hid_t dspace = H5Screate_simple( n_dims, dsdims, NULL );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:614:9: error: ‘offset’ was not declared in this scope
  
  offset[d] = dims_offset[d];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:618:11: error: expected ‘;’ before ‘fspace’
  
  hid_t fspace = H5Dget_space( dsdata );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:619:26: error: ‘fspace’ was not declared in this scope
  
  H5Sselect_hyperslab( fspace, H5S_SELECT_SET,
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:619:34: error: ‘H5S_SELECT_SET’ was not declared in this scope
  
  H5Sselect_hyperslab( fspace, H5S_SELECT_SET,
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:620:26: error: ‘offset’ was not declared in this scope
  
  offset, NULL, dsdims, NULL );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:620:40: error: ‘dsdims’ was not declared in this scope
  
  offset, NULL, dsdims, NULL );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:620:53: error: ‘H5Sselect_hyperslab’ was not declared in this scope
  
  offset, NULL, dsdims, NULL );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:623:11: error: expected ‘;’ before ‘dstype’
  
  hid_t dstype = GetH5type( matrix.type() );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:628:15: error: expected ‘;’ before ‘adims’
  
  hsize_t adims[1] = { channs };
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:629:7: error: ‘dstype’ was not declared in this scope
  
  dstype = H5Tarray_create( dstype, 1, adims );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:629:44: error: ‘adims’ was not declared in this scope
  
  dstype = H5Tarray_create( dstype, 1, adims );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:629:50: error: ‘H5Tarray_create’ was not declared in this scope
  
  dstype = H5Tarray_create( dstype, 1, adims );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:633:15: error: ‘dsdata’ was not declared in this scope
  
  H5Dwrite( dsdata, dstype, dspace, fspace,
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:633:23: error: ‘dstype’ was not declared in this scope
  
  H5Dwrite( dsdata, dstype, dspace, fspace,
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:633:31: error: ‘dspace’ was not declared in this scope
  
  H5Dwrite( dsdata, dstype, dspace, fspace,
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:634:15: error: ‘H5P_DEFAULT’ was not declared in this scope
  
  H5P_DEFAULT, matrix.data );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:634:40: error: ‘H5Dwrite’ was not declared in this scope
  
  H5P_DEFAULT, matrix.data );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:637:24: error: ‘H5Tclose’ was not declared in this scope
  
  H5Tclose( dstype );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:639:22: error: ‘H5Sclose’ was not declared in this scope
  
  H5Sclose( dspace );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:641:22: error: ‘H5Dclose’ was not declared in this scope
  
  H5Dclose( dsdata );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:580:9: warning: unused variable ‘channs’ [-Wunused-variable]
  
  int channs = matrix.channels();
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp: In member function ‘virtual void cv::hdf::HDF5Impl::dsinsert(cv::InputArray, cv::String, const int*, const int*) const’:
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:670:5: error: ‘hsize_t’ was not declared in this scope
  
  hsize_t dsdims[n_dims];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:671:13: error: expected ‘;’ before ‘offset’
  
  hsize_t offset[n_dims];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:675:7: error: ‘offset’ was not declared in this scope
  
  offset[d] = 0;
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:676:7: error: ‘dsdims’ was not declared in this scope
  
  dsdims[d] = matrix.size[d];

  ......
  hsize_t foffset[1] = 0  ;
  ^
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:1022:11 : error : expected ‘ ;’ before ‘dspace’
  hid_t dspace = H5Screate_simple( 1, dsddims, NULL ) ;
  ^
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:1025:26 : error : ‘dspace’ was not declared in this scope
  H5Sselect_hyperslab( dspace, H5S_SELECT_SET,
  ^
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:1025:34 : error : ‘H5S_SELECT_SET’ was not declared in this scope
  H5Sselect_hyperslab( dspace, H5S_SELECT_SET,
  ^
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:1026:26 : error : ‘foffset’ was not declared in this scope
  foffset, NULL, dsddims, NULL ) ;

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  File "/home/cocadas/anaconda2/bin/conda-build", line 6, in 
  
  sys.exit(conda_build.cli.main_build.main())
  
  File "/home/cocadas/anaconda2/lib/python2.7/site-packages/conda_build/cli/main_build.py", line 334, in main
  
  execute(sys.argv[1:])
  
  File "/home/cocadas/anaconda2/lib/python2.7/site-packages/conda_build/cli/main_build.py", line 325, in execute
  
  noverify=args.no_verify)
  
  File "/home/cocadas/anaconda2/lib/python2.7/site-packages/conda_build/api.py", line 97, in build
  
  need_source_download=need_source_download, config=config)
  
  File "/home/cocadas/anaconda2/lib/python2.7/site-packages/conda_build/build.py", line 1502, in build_tree
  
  config=config)
  
  File "/home/cocadas/anaconda2/lib/python2.7/site-packages/conda_build/build.py", line 1137, in build
  
  utils.check_call_env(cmd, env=env, cwd=src_dir)
  
  File "/home/cocadas/anaconda2/lib/python2.7/site-packages/conda_build/utils.py", line 616, in check_call_env
  
  return _func_defaulting_env_to_os_environ(subprocess.check_call, *popenargs, **kwargs)
  
  File "/home/cocadas/anaconda2/lib/python2.7/site-packages/conda_build/utils.py", line 612, in _func_defaulting_env_to_os_environ
  
  return func(_args, **kwargs)
  
  File "/home/cocadas/anaconda2/lib/python2.7/subprocess.py", line 186, in check_call
  
  raise CalledProcessError(retcode, cmd)
  
  subprocess.CalledProcessError: Command '['/bin/bash', '-x', '-e', '/home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/conda_build.sh']' returned non-zero exit status 2

• 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.

• 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.