Recherche avancée

Sur d’autres sites (7050)

• Privacy in Business : What Is It and Why Is It Important ?

  13 juillet 2022, par Erin — Privacy

  Privacy concerns loom large among consumers. Yet, businesses remain reluctant to change the old ways of doing things until they become an operational nuisance. 

  More and more businesses are slowly starting to feel the pressure to incorporate privacy best practices. But what exactly does privacy mean in business ? And why is it important for businesses to protect users’ privacy ? 

  In this blog, we’ll answer all of these questions and more. 

  What is Privacy in Business ?

  In the corporate world, privacy stands for the business decision to use collected consumer data in a safe, secure and compliant way. 

  Companies with a privacy-centred culture : 

  • Get explicit user consent to tracking, opt-ins and data sharing 
  • Collect strictly necessary data in compliance with regulations 
  • Ask for permissions to collect, process and store sensitive data 
  • Provide transparent explanations about data operationalisation and usage 
  • Have mechanisms for data collection opt-outs and data removal requests 
  • Implement security controls for storing collected data and limit access permissions to it 

  In other words : They treat consumers’ data with utmost integrity and security – and provide reassurances of ethical data usage. 

  What Are the Ethical Business Issues Related to Privacy ?

  Consumer data analytics has been around for decades. But digital technologies – ubiquitous connectivity, social media networks, data science and machine learning – increased the magnitude and sophistication of customer profiling.

  Big Tech companies like Google and Facebook, among others, capture millions of data points about users. These include general demographics data like “age” or “gender”, as well as more granular insights such as “income”, “past browsing history” or “recently visited geo-locations”. 

  When combined, such personally identifiable information (PII) can be used to approximate the user’s exact address, frequently purchased goods, political beliefs or past medical conditions. Then such information is shared with third parties such as advertisers. 

  That’s when ethical issues arise. 

  The Cambridge Analytica data scandal is a prime example of consumer data that was unethically exploited. 

  Over the years, Google also faced a series of regulatory issues surrounding consumer privacy breaches :

  • In 2021, a Google Chrome browser update put some 2.6 billion users at risk of “surveillance, manipulation and abuse” by providing third parties with data on device usage. 
  • The same year, Google was taken to court for failing to provide full disclosures on tracking performed in Google Chrome incognito mode. A $5 billion lawsuit is still pending.
  • As of 2022, Google Analytics 4 is considered GDPR non-compliant and was branded “illegal” by several European countries. 

  If you are curious, learn more about Google Analytics privacy issues. 

  The bigger issue ? Big Tech companies make the businesses that use their technologies (unknowingly) complicit in consumer data violations.

  In 2022, the Belgian data regulator found the official IAB Europe framework for user consent gathering in breach of GDPR. The framework was used by all major AdTech platforms to issue pop-ups for user consent to tracking. Now ad platforms must delete all data gathered through these. Biggest advertisers such as Procter & Gamble, Unilever, IBM and Mastercard among others, also received a notice about data removal and a regulatory warning on further repercussions if they fail to comply. 

  Big Tech firms have given brands unprecedented access to granular consumer data. Unrestricted access, however, also opened the door to data abuse and unethical use. 

  Examples of Unethical Data Usage by Businesses 

  • Data hoarding means excessively harvesting all available consumer data because a possibility to do so exists, often using murky consent mechanisms. Yet, 85% of collected Big Data is either dark or redundant, obsolete or trivial (ROT).
  • Invasive personalisation based on sensitive user information (or second-guesses), like a recent US marketing campaign, congratulating women on pregnancy (even if they weren’t expecting). Overall, 75% of consumers find most forms of personalisation somewhat creepy. 22% also said they’d leave for another brand due to creepy experiences.
  • Hyper-targeted advertising campaigns based on data consumers would prefer not to share. A recent investigation found that advertising platforms often assign sensitive labels to users (as part of their ad profiles), indicative of their religion, mental issues, history with abuse and so on. This allows advertisers to target such consumers with dubious ads. 

  Ultimately, excessive data collection, paired with poor data protection in business settings, results in major data breaches and costly damage control. Given that cyber attacks are on the rise, every business is vulnerable. 

  Why Should a Business Be Concerned About Protecting the Privacy of Its Customers ?

  Businesses must prioritise customer privacy because that’s what is expected of them. Globally, 89% of consumers say they care about their privacy. 

  As frequent stories about unethical data usage, excessive tracking and data breaches surface online, even more grow more concerned about protecting their data. Many publicly urge companies to take action. Others curtail their relationships with brands privately. 

  

  On average, 45% of consumers feel uncomfortable about sharing personal data. According to KPMG, 78% of American consumers have fears about the amount of data being collected. 40% of them also don’t trust companies to use their data ethically. Among Europeans, 41% are unwilling to share any personal data with businesses. 

  Because the demand for online privacy is rising, progressive companies now treat privacy as a competitive advantage. 

  For example, the encrypted messaging app Signal gained over 42 million active users in a year because it offers better data security and privacy protection. 

  ProtonMail, a privacy-centred email client, also amassed a 50 million user base in several years thanks to a “fundamentally stronger definition of privacy”.

  The growth of privacy-mindful businesses speaks volumes. And even more good things happen to privacy-mindful businesses : 

  • Higher consumer trust and loyalty 
  • Improved attractiveness to investors
  • Less complex compliance
  • Minimum cybersecurity exposure 
  • Better agility and innovation

  It’s time to start pursuing them ! Learn how to embed privacy and security into your operations.

• aarch64 : vp9 : Implement NEON loop filters

  13 novembre 2016, par Martin Storsjö

  aarch64 : vp9 : Implement NEON loop filters
  This work is sponsored by, and copyright, Google.
  These are ported from the ARM version ; thanks to the larger
  
  amount of registers available, we can do the loop filters with
  
  16 pixels at a time. The implementation is fully templated, with
  
  a single macro which can generate versions for both 8 and
  
  16 pixels wide, for both 4, 8 and 16 pixels loop filters
  
  (and the 4/8 mixed versions as well).
  For the 8 pixel wide versions, it is pretty close in speed (the
  
  v_4_8 and v_8_8 filters are the best examples of this ; the h_4_8
  
  and h_8_8 filters seem to get some gain in the load/transpose/store
  
  part). For the 16 pixels wide ones, we get a speedup of around
  
  1.2-1.4x compared to the 32 bit version.
  Examples of runtimes vs the 32 bit version, on a Cortex A53 :
  
                                         ARM AArch64
  
  vp9_loop_filter_h_4_8_neon :          144.0   127.2
  
  vp9_loop_filter_h_8_8_neon :          207.0   182.5
  
  vp9_loop_filter_h_16_8_neon :         415.0   328.7
  
  vp9_loop_filter_h_16_16_neon :        672.0   558.6
  
  vp9_loop_filter_mix2_h_44_16_neon :   302.0   203.5
  
  vp9_loop_filter_mix2_h_48_16_neon :   365.0   305.2
  
  vp9_loop_filter_mix2_h_84_16_neon :   365.0   305.2
  
  vp9_loop_filter_mix2_h_88_16_neon :   376.0   305.2
  
  vp9_loop_filter_mix2_v_44_16_neon :   193.2   128.2
  
  vp9_loop_filter_mix2_v_48_16_neon :   246.7   218.4
  
  vp9_loop_filter_mix2_v_84_16_neon :   248.0   218.5
  
  vp9_loop_filter_mix2_v_88_16_neon :   302.0   218.2
  
  vp9_loop_filter_v_4_8_neon :           89.0    88.7
  
  vp9_loop_filter_v_8_8_neon :          141.0   137.7
  
  vp9_loop_filter_v_16_8_neon :         295.0   272.7
  
  vp9_loop_filter_v_16_16_neon :        546.0   453.7
  The speedup vs C code in checkasm tests is around 2-7x, which is
  
  pretty much the same as for the 32 bit version. Even if these functions
  
  are faster than their 32 bit equivalent, the C version that we compare
  
  to also became around 1.3-1.7x faster than the C version in 32 bit.
  Based on START_TIMER/STOP_TIMER wrapping around a few individual
  
  functions, the speedup vs C code is around 4-5x.
  Examples of runtimes vs C on a Cortex A57 (for a slightly older version
  
  of the patch) :
  
                           A57 gcc-5.3  neon
  
  loop_filter_h_4_8_neon :        256.6  93.4
  
  loop_filter_h_8_8_neon :        307.3 139.1
  
  loop_filter_h_16_8_neon :       340.1 254.1
  
  loop_filter_h_16_16_neon :      827.0 407.9
  
  loop_filter_mix2_h_44_16_neon : 524.5 155.4
  
  loop_filter_mix2_h_48_16_neon : 644.5 173.3
  
  loop_filter_mix2_h_84_16_neon : 630.5 222.0
  
  loop_filter_mix2_h_88_16_neon : 697.3 222.0
  
  loop_filter_mix2_v_44_16_neon : 598.5 100.6
  
  loop_filter_mix2_v_48_16_neon : 651.5 127.0
  
  loop_filter_mix2_v_84_16_neon : 591.5 167.1
  
  loop_filter_mix2_v_88_16_neon : 855.1 166.7
  
  loop_filter_v_4_8_neon :        271.7  65.3
  
  loop_filter_v_8_8_neon :        312.5 106.9
  
  loop_filter_v_16_8_neon :       473.3 206.5
  
  loop_filter_v_16_16_neon :      976.1 327.8
  The speed-up compared to the C functions is 2.5 to 6 and the cortex-a57
  
  is again 30-50% faster than the cortex-a53.
  Signed-off-by : Martin Storsjö <martin@martin.st>
  

  • [DBH] libavcodec/aarch64/Makefile
  • [DBH] libavcodec/aarch64/vp9dsp_init_aarch64.c
  • [DBH] libavcodec/aarch64/vp9lpf_neon.S

• aarch64 : vp9 : Implement NEON loop filters

  14 novembre 2016, par Martin Storsjö

  aarch64 : vp9 : Implement NEON loop filters
  This work is sponsored by, and copyright, Google.
  These are ported from the ARM version ; thanks to the larger
  
  amount of registers available, we can do the loop filters with
  
  16 pixels at a time. The implementation is fully templated, with
  
  a single macro which can generate versions for both 8 and
  
  16 pixels wide, for both 4, 8 and 16 pixels loop filters
  
  (and the 4/8 mixed versions as well).
  For the 8 pixel wide versions, it is pretty close in speed (the
  
  v_4_8 and v_8_8 filters are the best examples of this ; the h_4_8
  
  and h_8_8 filters seem to get some gain in the load/transpose/store
  
  part). For the 16 pixels wide ones, we get a speedup of around
  
  1.2-1.4x compared to the 32 bit version.
  Examples of runtimes vs the 32 bit version, on a Cortex A53 :
  
                                         ARM AArch64
  
  vp9_loop_filter_h_4_8_neon :          144.0   127.2
  
  vp9_loop_filter_h_8_8_neon :          207.0   182.5
  
  vp9_loop_filter_h_16_8_neon :         415.0   328.7
  
  vp9_loop_filter_h_16_16_neon :        672.0   558.6
  
  vp9_loop_filter_mix2_h_44_16_neon :   302.0   203.5
  
  vp9_loop_filter_mix2_h_48_16_neon :   365.0   305.2
  
  vp9_loop_filter_mix2_h_84_16_neon :   365.0   305.2
  
  vp9_loop_filter_mix2_h_88_16_neon :   376.0   305.2
  
  vp9_loop_filter_mix2_v_44_16_neon :   193.2   128.2
  
  vp9_loop_filter_mix2_v_48_16_neon :   246.7   218.4
  
  vp9_loop_filter_mix2_v_84_16_neon :   248.0   218.5
  
  vp9_loop_filter_mix2_v_88_16_neon :   302.0   218.2
  
  vp9_loop_filter_v_4_8_neon :           89.0    88.7
  
  vp9_loop_filter_v_8_8_neon :          141.0   137.7
  
  vp9_loop_filter_v_16_8_neon :         295.0   272.7
  
  vp9_loop_filter_v_16_16_neon :        546.0   453.7
  The speedup vs C code in checkasm tests is around 2-7x, which is
  
  pretty much the same as for the 32 bit version. Even if these functions
  
  are faster than their 32 bit equivalent, the C version that we compare
  
  to also became around 1.3-1.7x faster than the C version in 32 bit.
  Based on START_TIMER/STOP_TIMER wrapping around a few individual
  
  functions, the speedup vs C code is around 4-5x.
  Examples of runtimes vs C on a Cortex A57 (for a slightly older version
  
  of the patch) :
  
                           A57 gcc-5.3  neon
  
  loop_filter_h_4_8_neon :        256.6  93.4
  
  loop_filter_h_8_8_neon :        307.3 139.1
  
  loop_filter_h_16_8_neon :       340.1 254.1
  
  loop_filter_h_16_16_neon :      827.0 407.9
  
  loop_filter_mix2_h_44_16_neon : 524.5 155.4
  
  loop_filter_mix2_h_48_16_neon : 644.5 173.3
  
  loop_filter_mix2_h_84_16_neon : 630.5 222.0
  
  loop_filter_mix2_h_88_16_neon : 697.3 222.0
  
  loop_filter_mix2_v_44_16_neon : 598.5 100.6
  
  loop_filter_mix2_v_48_16_neon : 651.5 127.0
  
  loop_filter_mix2_v_84_16_neon : 591.5 167.1
  
  loop_filter_mix2_v_88_16_neon : 855.1 166.7
  
  loop_filter_v_4_8_neon :        271.7  65.3
  
  loop_filter_v_8_8_neon :        312.5 106.9
  
  loop_filter_v_16_8_neon :       473.3 206.5
  
  loop_filter_v_16_16_neon :      976.1 327.8
  The speed-up compared to the C functions is 2.5 to 6 and the cortex-a57
  
  is again 30-50% faster than the cortex-a53.
  This is an adapted cherry-pick from libav commits
  
  9d2afd1eb8c5cc0633062430e66326dbf98c99e0 and
  
  31756abe29eb039a11c59a42cb12e0cc2aef3b97.
  Signed-off-by : Ronald S. Bultje <rsbultje@gmail.com>
  

  • [DH] libavcodec/aarch64/Makefile
  • [DH] libavcodec/aarch64/vp9dsp_init_aarch64.c
  • [DH] libavcodec/aarch64/vp9lpf_neon.S