Angela  Dickens

Angela Dickens

1593831240

Facebook Privacy Glitch Gave 5K Developers Access to ‘Expired’ Data

Facebook has fixed a privacy issue that gave developers access to user data long after the 90-day “expiration” date.

Facebook is facing yet another privacy faux pas in how its users’ data is collected and used by third-party apps. The social media giant said that it recently discovered that 5,000 developers received data from Facebook users — long after their access to that data should have expired.

In 2018, on the heels of the Cambridge Analytica privacy incident, Facebook debuted stricter controls over data collection by third-party app developers. As part of that, Facebook announced it would automatically expire an app’s ability to receive a user’s data if they hadn’t used the app in 90 days.

However, recently, “we discovered that in some instances apps continued to receive the data that people had previously authorized, even if it appeared they hadn’t used the app in the last 90 days,” said Konstantinos Papamiltiadis, vice president of Platform Partnerships with Facebook, in a Wednesday post.

For example, “this could happen if someone used a fitness app to invite their friends from their hometown to a workout, but we didn’t recognize that some of their friends had been inactive for many months,” he said.

Facebook estimates that 5,000 developers were able to continually receive information (such as language or gender) on “inactive” app users, in this manner. It has since fixed the issue.

The company said it hasn’t seen evidence that this issue resulted in sharing information that was inconsistent with the permissions people gave when they logged in using Facebook, however.

Facebook’s privacy troubles began in 2018 after its Cambridge Analytica privacy snafu. After that, the company said it suspended tens of thousands of apps as part of its ongoing investigation into how third-party apps on its platform collect, handle and utilize users’ personal data. And then in 2019, Facebook found that 100 third-party app developers improperly accessed the names and profile pictures of members in various Facebook groups.

“Facebook is a data-aggregation company first and foremost. Given this, it’s of no surprise that slip ups occasionally occur around the handling of the vast amount of raw and post-processed data they house,” Jonn Callahan, principal AppSec consultant at nVisium, told Threatpost. “This is especially true given their track record. It’s clear that proper handling of the collected data comes second to the monetization of the data.”

To bolster its privacy policies, earlier in June, Facebook said it had started to report its privacy practices to a newly formed, independent Privacy Committee. The creation of the independent committee was part of the company’s settlement a year ago with the Federal Trade Commission (FTC) over data-privacy violations, which came in addition to a $5 billion fine (which was derided as “chump change” by lawmakers and privacy analysts).

Facebook said on Wednesday it would attempt to further tighten its policies around third-party data collection by providing developers with clearer guidance around data usage and sharing.

“Today we’re also introducing new Platform Terms and Developer Policies to ensure businesses and developers clearly understand their responsibility to safeguard data and respect people’s privacy when using our platform,” Papamiltiadis said. “These new terms limit the information developers can share with third parties without explicit consent from people. They also strengthen data security req

#facebook #privacy #data #developer #security #social media #third party app

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Buddha Community

Facebook Privacy Glitch Gave 5K Developers Access to ‘Expired’ Data
Angela  Dickens

Angela Dickens

1593831240

Facebook Privacy Glitch Gave 5K Developers Access to ‘Expired’ Data

Facebook has fixed a privacy issue that gave developers access to user data long after the 90-day “expiration” date.

Facebook is facing yet another privacy faux pas in how its users’ data is collected and used by third-party apps. The social media giant said that it recently discovered that 5,000 developers received data from Facebook users — long after their access to that data should have expired.

In 2018, on the heels of the Cambridge Analytica privacy incident, Facebook debuted stricter controls over data collection by third-party app developers. As part of that, Facebook announced it would automatically expire an app’s ability to receive a user’s data if they hadn’t used the app in 90 days.

However, recently, “we discovered that in some instances apps continued to receive the data that people had previously authorized, even if it appeared they hadn’t used the app in the last 90 days,” said Konstantinos Papamiltiadis, vice president of Platform Partnerships with Facebook, in a Wednesday post.

For example, “this could happen if someone used a fitness app to invite their friends from their hometown to a workout, but we didn’t recognize that some of their friends had been inactive for many months,” he said.

Facebook estimates that 5,000 developers were able to continually receive information (such as language or gender) on “inactive” app users, in this manner. It has since fixed the issue.

The company said it hasn’t seen evidence that this issue resulted in sharing information that was inconsistent with the permissions people gave when they logged in using Facebook, however.

Facebook’s privacy troubles began in 2018 after its Cambridge Analytica privacy snafu. After that, the company said it suspended tens of thousands of apps as part of its ongoing investigation into how third-party apps on its platform collect, handle and utilize users’ personal data. And then in 2019, Facebook found that 100 third-party app developers improperly accessed the names and profile pictures of members in various Facebook groups.

“Facebook is a data-aggregation company first and foremost. Given this, it’s of no surprise that slip ups occasionally occur around the handling of the vast amount of raw and post-processed data they house,” Jonn Callahan, principal AppSec consultant at nVisium, told Threatpost. “This is especially true given their track record. It’s clear that proper handling of the collected data comes second to the monetization of the data.”

To bolster its privacy policies, earlier in June, Facebook said it had started to report its privacy practices to a newly formed, independent Privacy Committee. The creation of the independent committee was part of the company’s settlement a year ago with the Federal Trade Commission (FTC) over data-privacy violations, which came in addition to a $5 billion fine (which was derided as “chump change” by lawmakers and privacy analysts).

Facebook said on Wednesday it would attempt to further tighten its policies around third-party data collection by providing developers with clearer guidance around data usage and sharing.

“Today we’re also introducing new Platform Terms and Developer Policies to ensure businesses and developers clearly understand their responsibility to safeguard data and respect people’s privacy when using our platform,” Papamiltiadis said. “These new terms limit the information developers can share with third parties without explicit consent from people. They also strengthen data security req

#facebook #privacy #data #developer #security #social media #third party app

 iOS App Dev

iOS App Dev

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Sid  Schuppe

Sid Schuppe

1617955288

Benefits of Data Ingestion

In the last two decades, many businesses have had to change their models as business operations continue to complicate. The major challenge companies face today is that a large amount of data is generated from multiple data sources. So, data analytics have introduced filters to various data sources to detect this problem. They need analytics and business intelligence to access all their data sources to make better business decisions.

It is obvious that the company needs this data to make decisions based on predicted market trends, market forecasts, customer requirements, future needs, etc. But how do you get all your company data in one place to make a proper decision? Data ingestion consolidates your data and stores it in one place.

#big data #data access #data ingestion #data collection #batch processing #data access layer #data integration platform #automate data collection

Macey  Kling

Macey Kling

1597579680

Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data