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• France rules Google Analytics non-compliant with GDPR

  11 février 2022, par Erin — Privacy

  Breaking news : The French Data Protection Agency, CNIL (Commission nationale de l’informatique et des libertés), has concluded that the use of Google Analytics is illegal under GDPR. The CNIL has begun issuing formal notices to website managers using Google Analytics.

  This follows the January 2022 Austrian Data Protection Authority’s decision to declare Google Analytics illegal to use under GDPR.

  Google Analytics GDPR breaches continue to spread through the EU

  Since the invalidation of the Privacy Shield framework, an agreement between the EU and US that allowed the transfer of data to certified US companies, the CNIL and other EU data protection authorities have received numerous complaints regarding data transfers collected during visits to websites using Google Analytics.

  "It’s interesting to see that the different European Data Protection Authorities all come to the same conclusion : the use of Google Analytics is illegal. There is a European task force and we assume that this action is coordinated and other authorities will decide similarly."

  About the CNIL’s decision

  In this model case, the CNIL has found that an unnamed website’s use of Google Analytics is non-compliant with GDPR because it had breached Article 44 which prohibits the transfer of personal data beyond the EU, unless the recipient country can prove adequate data protection. 

  Under the GDPR, personal data covers a range of identifiers including email address, race, gender, phone number to name a few, but the less obvious identifiers include IP addresses or cookie IDs, for instance. 

  The CNIL’s decision was based on the fact that the US does not meet GDPR sufficient levels of data protection as a result of US surveillance laws. Therefore, the unnamed website’s use of Google Analytics created risks for their website visitors when their personal data was exported to the US. 

  At the time of writing, it is unknown if the CNIL has issued a fine for the GDPR breach. However, the website manager of the unnamed website has been ordered by the CNIL to comply with the GDPR and, if necessary, stop using Google Analytics under the current conditions.

  "One thing we’re certain of is that these decisions will continue to roll out throughout the EU and potentially beyond.

  Other countries are imposing their own privacy regulations that closely mirror the GDPR like Brazil’s General Data Protection Law (LGPD), India’s Data Protection Bill, New Zealand’s Privacy Act and Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA) to name a few.”

  The CNIL offers an evaluation programme to help website managers determine whether web analytics solutions are exempt from collecting data prior to users’ agreement to opt-in through consent screens. Matomo, for instance, is a leading Google Analytics alternative that has been recommended by CNIL and is exempt from tracking consent. 

  

  English translation : “This is why I anticipated this announcement, gradually moving the analytics of my sites to @matomo_org since several weeks !

  “The @CNIL believes that the use of @googleanalytics is a violation of #GDPR”

  Immediate action required for Google Analytics users

  The CNIL and other EU-based data protection authorities have made their stance on Google Analytics clear and inaction will likely result in fines, which under the GDPR, can be up to €20 million or 4% of the organisation’s global turnover – whichever is higher.

  Based on the CNIL’s formal notice to the model case’s website manager, Google Analytics users should take immediate action to remove any chances of personal data being transferred to the US or find a Google Analytics alternative that is GDPR compliant. 

  

  English translation : “The CNIL considers that the use of Google Analytics is a violation of the GDPR. I use @matomo_org and I welcome it *winking face* It will squeal tires among growthackers who are slaughtering. Opportunity to look at alternative tools”

  Ready to begin your journey to GDPR compliance with Matomo ? Start your 21-day free trial now (no credit card required) and take advantage of our Google Analytics importer so you don’t lose any of your historical data. 

  Try 21-day free Cloud trial

  What does this mean for Matomo users ?

  As the GDPR continues to evolve, our users can rest assured that Matomo will be at the forefront of these changes. With Matomo Cloud, all data is stored in the EU or in your country of choice when you self-host on your own servers with Matomo On-Premise.

  Conclusion

  Google is in the EU’s crosshairs and organisations that continue to use their tools will be the one’s left to clean up the mess – not Google. Now is the time to act. Search for a Google Analytics alternative and close your compliance gaps today. 

  Join over 1 million other websites using Matomo now. Give Matomo a try with a 21-day free trial – no credit card required. 

  Try 21-day free Cloud trial

  We’d like to also bring attention to the privacy-fighting efforts from noyb and Max Schrems, as this should not go unnoticed. noyb is an independent, non-profit organisation that relies on the support of individuals. Support privacy by supporting noyb – donate or become a member now. 

  Contact details for media :

  For quotes or interviews, please email marketing@matomo.org

• 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

• How to not process any personal data with Matomo and what it means for you

  22 avril 2018, par InnoCraft

  Disclaimer : this blog post has been written by digital analysts, not lawyers. The purpose of this article is to explain how to not process any personal data with Matomo in order to avoid going through the GDPR compliance process with Matomo analytics. This work comes from our interpretation of different sources : the official GDPR text and the UK privacy commission : ICO resources. It cannot be considered as a professional legal advice. So as GDPR, this information is subject to change. GDPR may be also known as RGPD in French, Spanish, Portuguese, Datenschutz-Grundverordnung, DS-GVO in German, Algemene verordening gegevensbescherming in Dutch, Regolamento generale sulla protezione dei dati in Italian.

  Are you looking for a way to not process any personal data with Matomo ? If the answer is yes, you are at the right place. From our understanding, if you are not processing personal data, then you shouldn’t be concerned about GDPR. Our inspiration came from this official reference :

  “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 Regulation does not therefore concern the processing of such anonymous information, including for statistical or research purposes.“

  In this blog post we are going to see how you can configure Matomo in order to not process any personal data and what the consequences are.

  Which data is considered as personal according to GDPR ?

  From : eur-lex.europa.eu

  (1) “‘personal data’ means any information relating to an identified or identifiable natural person (‘data subject’) ; an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person ;”

  (30) “Natural persons may be associated with online identifiers provided by their devices, applications, tools and protocols, such as internet protocol addresses, cookie identifiers or other identifiers such as radio frequency identification tags. This may leave traces which, in particular when combined with unique identifiers and other information received by the servers, may be used to create profiles of the natural persons and identify them.”

  So according to your Matomo configuration, it may leave some traces within the following data :

  1. IP addresses
  2. Cookies identifiers
  3. Page URL or page titles
  4. User ID and Custom “personal” data
  5. Ecommerce order IDs
  6. Location
  7. Heatmaps & Session Recordings

  Let’s see each of them in more detail.

  1. IP addresses

  IP addresses can indirectly identify an individual. It can also give a good approximation of an individual’s location.

  IP addresses are therefore considered as personal data which means you need to anonymize them. To do so, a feature is available within Matomo, where you can anonymize the IP. We recommend you to anonymize at least the last two bytes :

  

  See our configuration guide for more information

  What are the consequences of using this feature ?

  When applying IP anonymization on two bytes, you will no longer be able to see the full IP in the UI.

  Moreover, there is a small chance that 2 different visitors with the same device and software configuration will be identified as the same visitor if the anonymised IP address is the same for both.

  2. Cookies

  It is not clear for us yet if all cookies are considered equal under GDPR. At this stage it is too early to make a definite decision.

  Did you know ? Matomo lets you optionally disable the creation of cookies by adding an extra line of code to your tracking code see below.

  

  See our configuration guide for more information

  What are the consequences of using this feature ?

  Matomo is using a few first party cookies, and the following cookies may hold personal data :

  • _pk_id : contains a visitor id used to identify unique visitors
  • _pk_ref : to identify from where they came from

  If Matomo cannot set cookies, it will use a technique called Fingerprint. It is based on several metadata such as the operating system, browser, browser plugins, IP address, browser language ; just to name a few to identify a unique visitor. As this feature is less accurate than the one using cookies, the number of visitors and visits will be affected.

  3. Page URLs and page titles

  URLs are not mentioned within the official GDPR text. However, we know that according to the different CMS you use, some of them may have URLs including personal identifiers.

  For example :

  

  As a result, you need to find a way to anonymize this data.

  There are several ways you can perform this action according to your website. If your website is adding the personal data through query parameters, you can define a rule to exclude them from Matomo.

  If the personal data are not included within query parameters, you can use the “setCustomURL” feature and write your code as follow :

  

  See our developer documentation for more information

  If you are also processing personal data within the title tag, you can use the following function : “setDocumentTitle”.

  What are the consequences of using this feature ?

  By anonymizing the URLs containing personal data, some of your  URLs will be grouped together.

  4. User ID and custom personal data

  User ID is a feature (a tracking code needs to be added) which allows you to identify the same user across different devices.

  A User ID needs a corresponding database in order to link a user across different devices, it can be an email, a username, a name, a random number… All those data are either direct or non direct online identifiers and are therefore under the scope of GDPR.

  It will be the same situation if you are using custom variables and/or custom dimensions in order to push personal data to the system.

  To continue using the User ID feature but not recording personal data, you can consider using a hash function which will anonymize/convert your actual User ID into something like “3jrj3j34434834urj33j3”.

  Alternatively, you can enable the feature “Anonymise User IDs”. This feature will be available starting in Matomo 3.5.0 :

  

  What are the consequences of using this feature ?

  Under GDPR, User ID is personal data. Anonymizing the User ID using a hash function or our built-in functionality make the User Id pseudo-anonymous, which means it can’t be easily identified to a specific user. As a result, you will still get accurate visits and unique visitors metrics, and the Visitor Profile, but without tracking the original User ID which is personal data.

  5. Ecommerce order IDs

  Order IDs are the reference number assigned to the products/services bought by your customers. As this information can be crossed with your internal database, it is considered as an online identifier and is therefore under the scope of GDPR. As for User ID, you can anonymize order IDs using our built-in functionality to Anonymise Order IDs (see section 4. about User Id).

  What are the consequences of anonymizing order ID ?

  It really depends on your former use of order IDs. If you were not using them in the past then you should not see any difference.

  6. Location

  Based on the IP address of a visitor, Matomo can detect the visitors location. Location data is problematic for privacy as this technology has become quite accurate and can detect not only the city a visitor is from, but sometimes an even more precise position of a visitor.

  

  In order to not leave any accurate traces, we strongly recommend you to enable the IP anonymization feature. Next, you need to enable the setting “Also use the anonymized IP address when enriching visits”. You find this setting directly below the IP anonymization. This is important as otherwise the full IP address will be used to geolocate a visitor.

  

  What are the consequences of anonymizing location data ?

  The more bytes you anonymize from the IP, the more anonymized your location will be. When you remove two bytes as suggested, the city and region location reports will not be as accurate. In some cases even the country may not be detected correctly anymore.

  7. Heatmaps & Session Recordings

  Heatmaps & Session Recording is a premium feature in Matomo allowing you to see where users click, hover, type and scroll. With session recordings you can then replay their actions in a video.

  Heatmaps & Session Recordings are under the scope of GDPR as they can disclose in some specific cases (for example : filling a contact form) personal data :

  

  To avoid this, Matomo will anonymize all keystrokes which a user enters into a form field unless you specifically whitelist a field. Many fields that could contain personal data, such as a credit card, phone number, email address, password, social security number, and more are always anonymized and not recorded.

  See our configuration guide for more information

  Note that a page may still show personal information within the page as part of regular content (not a form element). For example an address, or the profile page of a forum user. We have added a feature which allows you to set an HTML attribute “data-matomo-mask” to anonymize any personal content shown in the UI.

  What are the consequences of using this feature ?

  Mainly, you will not be able to see in plain text what people are entering into your forms.

  What should you do with past data ?

  Once more, we have to say that we are not lawyers. So do not take our answers as legal advice. From : ec.europa.eu/newsroom/article29/document.cfm ?doc_id=50053

  “For example, as the GDPR requires that a controller must be able to demonstrate that valid consent was obtained, all presumed consents of which no references are kept will automatically be below the consent standard of the GDPR and will need to be renewed.”

  Our interpretation is that, if you were previously relying on consent, unless you can demonstrate that valid consent was obtained, you need to get the consent back (which is almost impossible) or you need to anonymize or remove that data.

  To anonymize previously tracked data, we are actively working on a feature to do just that directly within Matomo. Alternatively, you may also set up the deletion of logs after a certain amount of time.

  We really hope you enjoyed reading this article. GDPR is still on the go and we are pretty sure you have a lot of questions about it. You probably would like to share our vision about it. So do not hesitate to ask us through our contact form to see how we are interpreting GDPR at Matomo and InnoCraft.

  The post How to not process any personal data with Matomo and what it means for you appeared first on Analytics Platform - Matomo.