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• What Is Data Misuse & How to Prevent It ? (With Examples)

  13 mai 2024, par Erin

  Your data is everywhere. Every time you sign up for an email list, log in to Facebook or download a free app onto your smartphone, your data is being taken.

  This can scare customers and users who fear their data will be misused.

  While data can be a powerful asset for your business, it’s important you manage it well, or you could be in over your head.

  In this guide, we break down what data misuse is, what the different types are, some examples of major data misuse and how you can prevent it so you can grow your brand sustainably.

  What is data misuse ?

  Data is a good thing.

  It helps analysts and marketers understand their customers better so they can serve them relevant information, products and services to improve their lives.

  But it can quickly become a bad thing for both the customers and business owners when it’s mishandled and misused.

  

  Data misuse is when a business uses data outside of the agreed-upon terms. When companies collect data, they need to legally communicate how that data is being used. 

  Who or what determines when data is being misused ?

  Several bodies :

  • User agreements
  • Data privacy laws
  • Corporate policies
  • Industry regulations

  There are certain laws and regulations around how you can collect and use data. Failure to comply with these guidelines and rules can result in several consequences, including legal action.

  Keep reading to discover the different types of data misuse and how to prevent it.

  3 types of data misuse

  There are a few different types of data misuse.

  If you fail to understand them, you could face penalties, legal trouble and a poor brand reputation.

  

  1. Commingling

  When you collect data, you need to ensure you’re using it for the right purpose. Commingling is when an organisation collects data from a specific audience for a specific reason but then uses the data for another purpose.

  One example of commingling is if a company shares sensitive customer data with another company. In many cases, sister companies will share data even if the terms of the data collection didn’t include that clause.

  Another example is if someone collects data for academic purposes like research but then uses the data later on for marketing purposes to drive business growth in a for-profit company.

  In either case, the company went wrong by not being clear on what the data would be used for. You must communicate with your audience exactly how the data will be used.

  2. Personal benefit

  The second common way data is misused in the workplace is through “personal benefit.” This is when someone with access to data abuses it for their own gain.

  The most common example of personal benefit data muse is when an employee misuses internal data.

  While this may sound like each instance of data misuse is caused by malicious intent, that’s not always the case. Data misuse can still exist even if an employee didn’t have any harmful intent behind their actions. 

  One of the most common examples is when an employee mistakenly moves data from a company device to personal devices for easier access.

  3. Ambiguity

  As mentioned above, when discussing commingling, a company must only use data how they say they will use it when they collect it.

  A company can misuse data when they’re unclear on how the data is used. Ambiguity is when a company fails to disclose how user data is being collected and used.

  This means communicating poorly on how the data will be used can be wrong and lead to misuse.

  One of the most common ways this happens is when a company doesn’t know how to use the data, so they can’t give a specific reason. However, this is still considered misuse, as companies need to disclose exactly how they will use the data they collect from their customers.

  Laws on data misuse you need to follow

  Data misuse can lead to poor reputations and penalties from big tech companies. For example, if you step outside social media platforms’ guidelines, you could be suspended, banned or shadowbanned.

  But what’s even more important is certain types of data misuse could mean you’re breaking laws worldwide. Here are some laws on data misuse you need to follow to avoid legal trouble :

  General Data Protection Regulation (GDPR)

  The GDPR, or General Data Protection Regulation, is a law within the European Union (EU) that went into effect in 2018.

  The GDPR was implemented to set a standard and improve data protection in Europe. It was also established to increase accountability and transparency for data breaches within businesses and organisations.

  The purpose of the GDPR is to protect residents within the European Union.

  The penalties for breaking GDPR laws are fines up to 20 million Euros or 4% of global revenues (whatever the higher amount is).

  The GDPR doesn’t just affect companies in Europe. You can break the GDPR’s laws regardless of where your organisation is located worldwide. As long as your company collects, processes or uses the personal data of any EU resident, you’re subject to the GDPR’s rules.

  If you want to track user data to grow your business, you need to ensure you’re following international data laws. Tools like Matomo—the world’s leading privacy-friendly web analytics solution—can help you achieve GDPR compliance and maintain it.

  With Matomo, you can confidently enhance your website’s performance, knowing that you’re adhering to data protection laws. 

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  California Consumer Privacy Act (CCPA)

  The California Consumer Privacy Act (CCPA) is another important data law companies worldwide must follow.

  Like GDPR, the CCPA is a data privacy law established to protect residents of a certain region — in this case, residents of California in the United States.

  The CCPA was implemented in 2020, and businesses worldwide can be penalised for breaking the regulations. For example, if you’re found violating the CCPA, you could be fined $7,500 for each intentional violation.

  If you have unintentional violations, you could still be fined, but at a lesser fee of $2,500.

  The Gramm-Leach-Bliley Act (GLBA)

  If your business is located within the United States, then you’re subject to a federal law implemented in 1999 called The Gramm-Leach-Bliley Act (GLB Act or GLBA).

  The GLBA is also known as the Financial Modernization Act of 1999. Its purpose is to control the way American financial institutions handle consumer data. 

  In the GLBA, there are three sections :

  1. The Financial Privacy Rule : regulates the collection and disclosure of private financial data.
  2. Safeguards Rule : Financial institutions must establish security programs to protect financial data.
  3. Pretexting Provisions : Prohibits accessing private data using false pretences.

  The GLBA also requires financial institutions in the U.S. to give their customers written privacy policy communications that explain their data-sharing practices.

  4 examples of data misuse in real life

  If you want to see what data misuse looks like in real life, look no further.

  Big tech is central to some of the biggest data misuses and scandals.

  

  Here are a few examples of data misuse in real life you should take note of to avoid a similar scenario :

  1. Facebook election interference

  One of history’s most famous examples of data misuse is the Facebook and Cambridge Analytica scandal in 2018.

  During the 2018 U.S. midterm elections, Cambridge Analytica, a political consulting firm, acquired personal data from Facebook users that was said to have been collected for academic research.

  Instead, Cambridge Analytica used data from roughly 87 million Facebook users. 

  This is a prime example of commingling.

  The result ? Cambridge Analytica was left bankrupt and dissolved, and Facebook was fined $5 billion by the Federal Trade Commission (FTC).

  2. Uber “God View” tracking

  Another big tech company, Uber, was caught misusing data a decade ago. 

  Why ?

  Uber implemented a new feature for its employees in 2014 called “God View.”

  The tool enabled Uber employees to track riders using their app. The problem was that they were watching them without the users’ permission. “God View” lets Uber spy on their riders to see their movements and locations.

  The FTC ended up slapping them with a major lawsuit, and as part of their settlement agreement, Uber agreed to have an outside firm audit their privacy practices between 2014 and 2034.

  

  3. Twitter targeted ads overstep

  In 2019, Twitter was found guilty of allowing advertisers to access its users’ personal data to improve advertisement targeting.

  Advertisers were given access to user email addresses and phone numbers without explicit permission from the users. The result was that Twitter ad buyers could use this contact information to cross-reference with Twitter’s data to serve ads to them.

  Twitter stated that the data leak was an internal error. 

  4. Google location tracking

  In 2020, Google was found guilty of not explicitly disclosing how it’s using its users’ personal data, which is an example of ambiguity.

  The result ?

  The French data protection authority fined Google $57 million.

  8 ways to prevent data misuse in your company

  Now that you know the dangers of data misuse and its associated penalties, it’s time to understand how you can prevent it in your company.

  

  Here are eight ways you can prevent data misuse :

  1. Track data with an ethical web analytics solution

  You can’t get by in today’s business world without tracking data. The question is whether you’re tracking it safely or not.

  If you want to ensure you aren’t getting into legal trouble with data misuse, then you need to use an ethical web analytics solution like Matomo.

  With it, you can track and improve your website performance while remaining GDPR-compliant and respecting user privacy. Unlike other web analytics solutions that monetise your data and auction it off to advertisers, with Matomo, you own your data.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  2. Don’t share data with big tech

  As the data misuse examples above show, big tech companies often violate data privacy laws.

  And while most of these companies, like Google, appear to be convenient, they’re often inconvenient (and much worse), especially regarding data leaks, privacy breaches and the sale of your data to advertisers.

  Have you ever heard the phrase : “You are the product ?” When it comes to big tech, chances are if you’re getting it for free, you (and your data) are the products they’re selling.

  The best way to stop sharing data with big tech is to stop using platforms like Google. For more ideas on different Google product alternatives, check out this list of Google alternatives.

  3. Identity verification 

  Data misuse typically isn’t a company-wide ploy. Often, it’s the lack of security structure and systems within your company. 

  An important place to start is to ensure proper identity verification for anyone with access to your data.

  4. Access management

  After establishing identity verification, you should ensure you have proper access management set up. For example, you should only give specific access to specific roles in your company to prevent data misuse.

  5. Activity logs and monitoring

  One way to track data misuse or breaches is by setting up activity logs to ensure you can see who is accessing certain types of data and when they’re accessing it.

  You should ensure you have a team dedicated to continuously monitoring these logs to catch anything quickly.

  6. Behaviour alerts 

  While manually monitoring data is important, it’s also good to set up automatic alerts if there is unusual activity around your data centres. You should set up behaviour alerts and notifications in case threats or compromising events occur.

  7. Onboarding, training, education

  One way to ensure quality data management is to keep your employees up to speed on data security. You should ensure data security is a part of your employee onboarding. Also, you should have regular training and education to keep people informed on protecting company and customer data.

  8. Create data protocols and processes 

  To ensure long-term data security, you should establish data protocols and processes. 

  To protect your user data, set up rules and systems within your organisation that people can reference and follow continuously to prevent data misuse.

  Leverage data ethically with Matomo

  Data is everything in business.

  But it’s not something to be taken lightly. Mishandling user data can break customer trust, lead to penalties from organisations and even create legal trouble and massive fines.

  You should only use privacy-first tools to ensure you’re handling data responsibly.

  Matomo is a privacy-friendly web analytics tool that collects, stores and tracks data across your website without breaking privacy laws.

  With over 1 million websites using Matomo, you can track and improve website performance with :

  • Accurate data (no data sampling)
  • Privacy-friendly and compliant with privacy regulations like GDPR, CCPA and more
  • Advanced features like heatmaps, session recordings, A/B testing and more

  Try Matomo free for 21-days. No credit card required.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

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• Maven dependency works on machine but not when packaged in a .war - org.bytedeco.javacpp.avutil

  26 décembre 2016, par Jake Miller

  I have a web app where you can upload a video and a thumbnail is created for this video. It works completely fine on my machine, but as soon as I deploy to AWS (Amazon Web Services) as a .war file, I experience dependency issues with maven.

  Here’s the exception from the logs :

  Caused by: java.lang.NoClassDefFoundError: Could not initialize class org.bytedeco.javacpp.avutil
  
      at java.lang.Class.forName0(Native Method) ~[na:1.8.0_101]
  
      at java.lang.Class.forName(Class.java:348) ~[na:1.8.0_101]
  
      at org.bytedeco.javacpp.Loader.load(Loader.java:472) ~[javacpp-1.2.1.jar!/:1.2.1]
  
      at org.bytedeco.javacpp.Loader.load(Loader.java:417) ~[javacpp-1.2.1.jar!/:1.2.1]
  
      at org.bytedeco.javacpp.avformat$AVFormatContext.<clinit>(avformat.java:2597) ~[ffmpeg-2.8.1-1.1.jar!/:1.2.1]
  
      at org.bytedeco.javacv.FFmpegFrameGrabber.startUnsafe(FFmpegFrameGrabber.java:391) ~[javacv-1.2.jar!/:1.2]
  
      at org.bytedeco.javacv.FFmpegFrameGrabber.start(FFmpegFrameGrabber.java:385) ~[javacv-1.2.jar!/:1.2]
  
      at com.myapp.app.service.ICampaignService.createThumbnail(ICampaignService.java:425) ~[classes!/:0.0.44T-SNAPSHOT]
  
      at com.myapp.app.service.ICampaignService$$FastClassBySpringCGLIB$$47736265.invoke(<generated>) ~[classes!/:0.0.44T-SNAPSHOT]
  
      at org.springframework.cglib.proxy.MethodProxy.invoke(MethodProxy.java:204) ~[spring-core-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.aop.framework.CglibAopProxy$CglibMethodInvocation.invokeJoinpoint(CglibAopProxy.java:720) ~[spring-aop-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.aop.framework.ReflectiveMethodInvocation.proceed(ReflectiveMethodInvocation.java:157) ~[spring-aop-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.transaction.interceptor.TransactionInterceptor$1.proceedWithInvocation(TransactionInterceptor.java:99) ~[spring-tx-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.transaction.interceptor.TransactionAspectSupport.invokeWithinTransaction(TransactionAspectSupport.java:280) ~[spring-tx-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.transaction.interceptor.TransactionInterceptor.invoke(TransactionInterceptor.java:96) ~[spring-tx-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.aop.framework.ReflectiveMethodInvocation.proceed(ReflectiveMethodInvocation.java:179) ~[spring-aop-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.aop.framework.CglibAopProxy$DynamicAdvisedInterceptor.intercept(CglibAopProxy.java:655) ~[spring-aop-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at com.myapp.app.service.ICampaignService$$EnhancerBySpringCGLIB$$67e59894.createThumbnail(<generated>) ~[classes!/:0.0.44T-SNAPSHOT]
  
      at com.myapp.app.controllers.CampaignController.uploadCampaign(CampaignController.java:237) ~[classes!/:0.0.44T-SNAPSHOT]
  
      at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) ~[na:1.8.0_101]
  
      at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) ~[na:1.8.0_101]
  
      at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) ~[na:1.8.0_101]
  
      at java.lang.reflect.Method.invoke(Method.java:498) ~[na:1.8.0_101]
  
      at org.springframework.web.method.support.InvocableHandlerMethod.doInvoke(InvocableHandlerMethod.java:221) ~[spring-web-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.web.method.support.InvocableHandlerMethod.invokeForRequest(InvocableHandlerMethod.java:136) ~[spring-web-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.web.servlet.mvc.method.annotation.ServletInvocableHandlerMethod.invokeAndHandle(ServletInvocableHandlerMethod.java:114) ~[spring-webmvc-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.web.servlet.mvc.method.annotation.RequestMappingHandlerAdapter.invokeHandlerMethod(RequestMappingHandlerAdapter.java:827) ~[spring-webmvc-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.web.servlet.mvc.method.annotation.RequestMappingHandlerAdapter.handleInternal(RequestMappingHandlerAdapter.java:738) ~[spring-webmvc-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.web.servlet.mvc.method.AbstractHandlerMethodAdapter.handle(AbstractHandlerMethodAdapter.java:85) ~[spring-webmvc-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      at org.springframework.web.servlet.DispatcherServlet.doDispatch(DispatcherServlet.java:963) ~[spring-webmvc-4.3.2.RELEASE.jar!/:4.3.2.RELEASE]
  
      ... 85 common frames omitted
  
  </generated></generated></clinit>

  Here are my maven dependencies for the ffmpeg wrapper I’m using :

  <dependency>
  
      <groupid>org.bytedeco</groupid>
  
      <artifactid>javacv</artifactid>
  
      <version>1.3</version>
  
  </dependency>
  
  <dependency>
  
      <groupid>org.bytedeco</groupid>
  
      <artifactid>javacpp</artifactid>
  
      <version>1.3</version>
  
  </dependency>

  I’ve tried various different versions but nothing has worked so far. What’s strange is the error says org.bytedeco.javacpp.avutil but I have access to the avutil class in my IDE.

  EDIT

  I’ve also tried everything at this github post : https://github.com/bytedeco/javacpp-presets/issues/225

  No solution I’ve found has worked. I’ve tried various different versions and various different org.bytedeco dependencies but they all yield the same error.

• FFMPEG concat video throws No Such Filter error

  5 janvier 2021, par Rohan Shah

  I am trying to concat three videos using FFMPEG,

  


  This is my command that I am executing using Java Runtime

  


      String command = "ffmpeg -i url_to_video -i url_to_video -i url_to_video -filter_complex [0:v] [0:a] [1:v] [1:a] [2:v] [2:a] concat=n=3:v=1:a=1 [v] [a] -map [v] -map [a] /home/rohan/output.mp4";

    Process process = Runtime.getRuntime().exec(command.split(" "));


  


  This is throwing me No Such Filter error, whereas if I try to run this via Terminal it works perfectly fine. I tried tweaking the cmd by removing or adding quotes, but any variant of this command that runs on terminal throws error in Java Runtime. While it runs perfectly on the Terminal.

  


  I have tried with Absolute paths and Amazon S3 URLs, while both of them work just fine in terminal, The S3 variant of command throws No Such Filter error and the Absolute path command throws a No Such File Or Directory error during Runtime.

  


  Here is the stack trace of the error

  


  Here is the standard error of the command (if any):

ffmpeg version n4.3.1 Copyright (c) 2000-2020 the FFmpeg developers
  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)
  configuration: --prefix= --prefix=/usr --disable-debug --disable-doc --disable-static --enable-cuda --enable-cuda-sdk --enable-cuvid --enable-libdrm --enable-ffplay --enable-gnutls --enable-gpl --enable-libass --enable-libfdk-aac --enable-libfontconfig --enable-libfreetype --enable-libmp3lame --enable-libnpp --enable-libopencore_amrnb --enable-libopencore_amrwb --enable-libopus --enable-libpulse --enable-sdl2 --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libv4l2 --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-libxcb --enable-libxvid --enable-nonfree --enable-nvenc --enable-omx --enable-openal --enable-opencl --enable-runtime-cpudetect --enable-shared --enable-vaapi --enable-vdpau --enable-version3 --enable-xlib
  libavutil      56. 51.100 / 56. 51.100
  libavcodec     58. 91.100 / 58. 91.100
  libavformat    58. 45.100 / 58. 45.100
  libavdevice    58. 10.100 / 58. 10.100
  libavfilter     7. 85.100 /  7. 85.100
  libswscale      5.  7.100 /  5.  7.100
  libswresample   3.  7.100 /  3.  7.100
  libpostproc    55.  7.100 / 55.  7.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'https://openxcell-development-public.s3.ap-south-1.amazonaws.com/bhit/outputForBHit.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2avc1mp41
    encoder         : Lavf58.45.100
  Duration: 00:00:10.57, start: 0.000000, bitrate: 2048 kb/s
    Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 1280x720 [SAR 1:1 DAR 16:9], 1696 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
    Metadata:
      handler_name    : VideoHandler
    Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, 5.1, fltp, 347 kb/s (default)
    Metadata:
      handler_name    : SoundHandler
Input #1, mov,mp4,m4a,3gp,3g2,mj2, from 'https://openxcell-development-public.s3.ap-south-1.amazonaws.com/bhit/outputForBHit.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2avc1mp41
    encoder         : Lavf58.45.100
  Duration: 00:00:10.57, start: 0.000000, bitrate: 2048 kb/s
    Stream #1:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 1280x720 [SAR 1:1 DAR 16:9], 1696 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
    Metadata:
      handler_name    : VideoHandler
    Stream #1:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, 5.1, fltp, 347 kb/s (default)
    Metadata:
      handler_name    : SoundHandler
Input #2, mov,mp4,m4a,3gp,3g2,mj2, from 'https://openxcell-development-public.s3.ap-south-1.amazonaws.com/bhit/outputForBHit.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2avc1mp41
    encoder         : Lavf58.45.100
  Duration: 00:00:10.57, start: 0.000000, bitrate: 2048 kb/s
    Stream #2:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 1280x720 [SAR 1:1 DAR 16:9], 1696 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
    Metadata:
      handler_name    : VideoHandler
    Stream #2:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, 5.1, fltp, 347 kb/s (default)
    Metadata:
      handler_name    : SoundHandler
[AVFilterGraph @ 0x564151595680] No such filter: ''
Error initializing complex filters.
Invalid argument