#dataanonymization

[luxlaff]

Do you trust artificial intelligence with the safety of your personal data? 🤖

Traditional anonymization methods are no longer cutting it. Your names, addresses, and even medical records could become fodder for AI's ravenous algorithms.

But there's a solution – synthetic data, homomorphic encryption, and blockchain. These technologies promise true privacy for machine learning. 🔐

Communities like @anon_tg and https://t.me/anon_club are already stirring, sensing this acute data privacy crisis. Want to learn more?

Tap the link in bio and join the vanguard of the fight for digital anonymity. The race is on! 🏁

[technology098]

Organizations collect data to improve their products and services and support their business. To use this data effectively, it must be shared with internal and external teams for various purposes. However, using this data for non-productive uses can lead to security breaches and compromised sensitive information.

[jjbizconsult]

Data for All, But Secure: Unlocking Data Democratization Responsibly ✨

[osintelligence]

https://bit.ly/40EW1gh - 🌐 In the realm of cyber threat intelligence, sharing is a fundamental practice. It enhances cybersecurity by informing teams about threat actors and their tactics. However, a recent panel discussion revealed that only 17% of security professionals in the financial services industry, typically early adopters of threat intel sharing, are very confident in their organization's level of sharing. Addressing this confidence gap is crucial for effective cybersecurity across all sectors. #CyberThreatIntelligence #CybersecuritySharing #FinancialServicesSecurity ⚖️ Regulatory compliance is driving a renewed focus on threat intelligence sharing. Initiatives like the White House's Executive Order on Improving the Nation’s Cybersecurity and the EU's upcoming Digital Operational Resilience Act (DORA) emphasize the importance of sharing information on cyber threats and vulnerabilities. This regulatory push aims to create a 'herd immunity' in cyberspace, where shared knowledge strengthens overall sector resilience. #RegulatoryCompliance #CybersecurityRegulation #DORA 🔗 When it comes to evolving threat intel sharing practices, there are key considerations to ensure effectiveness and confidence. User-friendly technology platforms that facilitate machine-to-machine sharing, data anonymization to address legal concerns about privacy and security, and mechanisms to foster trust within sharing communities are critical. These elements help in creating a nurturing environment for the exchange of contextualized threat intelligence. #ThreatIntelligenceSharing #DataAnonymization #TrustInCybersecurity 🛡️ The goal is not just to share threat intelligence but to do it effectively - determining how, what, where, and with whom to share. This approach is vital for enhancing collective and individual cybersecurity resilience.

[valevpn]

🔍 Does Grammarly Secure Your Personal Information with Integrity?

Grammarly is a popular writing assistance tool used by millions worldwide to improve their writing skills, grammar, and spelling. However, with its extensive usage comes concerns about the privacy and security of users' personal information. This article will delve into Grammarly's privacy practices and explore whether it truly protects your data with integrity.

Read on 👉 https://www.valevpn.com/post/does-grammarly-secure-your-personal-information-with-integrity

GrammarlyPrivacy #DataSecurity #PrivacyMatters #WritingAssistance #OnlinePrivacy #VPNProtection #PersonalDataProtection #DataAnonymization #DataEncryption #DeletePersonalData

[shaip]

3 Steps to Overcome Common AI Application Development Obstacles

From life-changing implementations like medical diagnostics imaging and self-driving vehicles to humble use cases such as virtual assistants or robot vacuums — artificial intelligence is being put to use to solve an incredible range of problems.

Despite widespread AI implementation efforts, however, the development of effective AI tools is still far from easy. Teams can expect to encounter quite a few obstacles along the way.

Data is one of the most important elements in developing an AI algorithm. Remember that just because data is being generated faster than ever before doesn’t mean the right data is easy to come by.

Low-quality, biased, or incorrectly annotated data can (at best) add another step. These extra steps will slow you down because the data science and development teams must work through these on the way to a functional application.

At worst, faulty data can sabotage a solution to the point where it’s no longer salvageable. Don’t believe it? That’s exactly how Amazon spent years building a sexist hiring tool that the company would eventually scrap.

Just Getting Started

Once you have high-quality data, your work is far from over. Instead, you’ll need to convert it into a machine-readable format — a process that comes with numerous challenges.

In highly regulated industries like finance and healthcare, for instance, data will need to be carefully de-identified to ensure it meets privacy standards.

If you’re sourcing international data, you’ll also need to adhere to data-sharing laws that govern the countries where the data originates. The process sounds like dotting the i’s and crossing the t’s — but adherence to data will require in-depth knowledge of a complex regulatory landscape.

Crunching the Numbers

Of course, data is nothing without a team to turn it into insights that can inform an AI model.

If your organization lacks a trained data science team in-house, you might have to hire or outsource these capabilities.

Even if you do have a team of experienced engineers on your roster, the sheer time required to annotate raw data can get in the way of actual algorithm development.

Employees aren’t likely to take a pay cut just because you have them performing lower-value work.

These obstacles certainly add complexity to the development process, but they shouldn’t be deal-breakers. Instead, a well-constructed plan can help you avoid some of these hurdles while you clear others one at a time as they appear.

3 Steps to Overcome Common AI Application Development Obstacles

REMEMBER: Maximize Efficiency and Outcomes

The AI development process is iterative, with each iteration is aimed at improving the accuracy and scope of the model. As you begin to plan how your own development journey will unfold, focus on the following three steps.

1. Find the right partner for primary tasks

Data sourcing, annotation, and de-identification can consume more than 80% of a data scientist’s time.

Leveraging the expertise of the right partner can save a huge amount of your AI team’s time and energy. You want to allow your team to utilize the skills you pay them for instead of performing mundane data-cleaning functions.

Besides ensuring your team is free to put their best skills to good use, an experienced partner can help you track down the highest-quality content for training your AI data model.

Gartner Research predicts that 85% of AI implementations through 2022 will produce errors in output due to bias in input. With the right partner helping you source and annotate data, you can avoid a costly scenario where “garbage in yields garbage out.”

2. Align stakeholders with clear use cases and customer needs

Building an AI solution is a considerable investment that will require lots of participants with varying roles.

Having a diverse range of experiences and perspectives is critical to a successful AI implementation, but only if these stakeholders are aligned on the project’s goal.

Existing gaps between different perceptions of the ideal outcome only widen as the development process progresses, so it’s important to take the time to nip these misunderstandings in the bud early.

Spend time with all stakeholders and teams to establish clearly defined goals and criteria for success. This small upfront investment will cost you time and money, but it will save you both in the long run by keeping participants aligned for the project’s duration.

3. Get it right, one implementation at a time

AI is extremely powerful, but it’s not a silver bullet; there are still many business problems for which AI isn’t a suitable solution. Instead of throwing artificial intelligence at the wall and seeing what sticks, organizations should start by prioritizing the use cases that make the most sense.

Are you looking to filter through a vast amount of data? AI is an excellent option. If you’re trying to spot patterns, it’s equally capable, and software can scale to outperform millions of human analysts with ease.

Start with simple or proven AI implementations that offer the easiest and quickest path to a payoff, and take the experience gained through these ventures to more complicated future projects.

Conclusion

Creating an AI application isn’t easy, but the potential rewards are massive. Keep a clear understanding of the potential pitfalls your team could encounter throughout the process.

Your potential pitfalls include data sourcing and annotation issues, personnel shortages, skills gaps, and a lack of alignment toward a common goal.

Construct a plan that takes these obstacles into account. Start with the above three steps, and you’ll be well on your way to an effective AI implementation.

[surveycircle]

Participants needed for online survey! Topic: "Survey on social media users' tolerance towards privacy invasion" https://t.co/MJdpjFdokT via @SurveyCircle#SocialMedia #privacy #DataAnonymity #security #DataProtection #research #survey #surveycircle pic.twitter.com/m9piPhcLSw

— Daily Research @SurveyCircle (@daily_research) January 9, 2020

[technology098]

Fortifying Data Masking: Strategies for Protecting Sensitive Information and Ensuring Compliance in Today's Digital Landscape

In today's data-driven world, organizations harness data to refine their offerings, enhance customer experiences, and fortify their operations. However, the strategic utilization of Data Masking its dissemination among internal and external stakeholders. While this sharing is crucial for fostering innovation and collaboration, it also introduces risks of security breaches and compromises to sensitive information. Consequently, safeguarding data integrity has become paramount in contemporary data environments.

To mitigate the peril of data breaches and uphold regulatory compliance, such as GDPR, PCI-DSS, and HIPAA, organizations must adopt robust security protocols. These measures are indispensable for curtailing the exposure of sensitive data throughout its lifecycle. By implementing stringent security practices, enterprises can fortify their defenses and uphold the trust of their customers and partners.

In adhering to security and compliance imperatives, organizations deploy a multifaceted approach encompassing various phases of their operational cycle. From data acquisition to storage, processing, and transmission, each stage demands tailored security measures to mitigate vulnerabilities and thwart potential threats. By integrating security seamlessly into their workflows, organizations can engender a culture of vigilance and resilience against evolving cybersecurity challenges.

Central to data masking is the concept of least privilege, whereby access to sensitive information is restricted only to authorized personnel and systems. By limiting access based on roles and responsibilities, organizations minimize the risk of unauthorized data exposure and inadvertent leaks. Moreover, encryption techniques are instrumental in safeguarding data integrity during transit and storage, rendering it indecipherable to unauthorized entities.

Furthermore, organizations invest in robust authentication and authorization mechanisms to validate the identity of users and govern their access privileges. Multi-factor authentication, biometric verification, and access controls are pivotal in fortifying defenses against unauthorized access attempts. Similarly, audit trails and logging mechanisms enable organizations to monitor and trace user activities, facilitating timely detection and mitigation of security incidents.

In parallel, organizations leverage advanced technologies such as artificial intelligence and machine learning to augment their threat detection capabilities. By analyzing vast troves of data in real-time, these systems can discern anomalous patterns indicative of potential security breaches. This proactive approach empowers organizations to preemptively thwart threats before they escalate into full-fledged breaches.

Moreover, robust incident response plans are indispensable for orchestrating swift and coordinated responses to security incidents. By delineating roles, responsibilities, and escalation procedures, organizations can minimize the impact of breaches and expedite recovery efforts. Regular drills and simulations enable teams to refine their response strategies and bolster their preparedness for emergent cyber threats.

Beyond technological fortifications, fostering a culture of security awareness is imperative for cultivating a vigilant workforce. Comprehensive training programs equip employees with the knowledge and skills to identify and mitigate security risks in their daily activities. By instilling a sense of collective responsibility, organizations empower their employees to serve as frontline defenders against cyber threats.

In conclusion, safeguarding sensitive data is an ongoing imperative for organizations navigating the complexities of today's Data Masking. By embracing a holistic approach to data security, encompassing technological, procedural, and cultural dimensions, enterprises can fortify their defenses and uphold the trust of their stakeholders. In a landscape fraught with evolving cyber threats, proactive measures are indispensable for preserving data integrity and fostering sustainable business growth.

[osintelligence]

https://politi.co/48vcuG1 - 🔍 A startling revelation over dinner led journalist Byron Tau on a deep dive into the U.S. government's legal but secretive acquisition of consumer data for surveillance purposes. This journey uncovers an intricate network of contractors selling vast amounts of personal information, raising concerns even among some officials. Despite the legal standing, the lack of substantial digital privacy reforms underscores a significant privacy dilemma. #DataPrivacy #Surveillance #DigitalEra 📘 In "Means of Control," Tau elucidates the extent of government surveillance, employing purchased data from cellphones, social media, and more for purposes ranging from law enforcement to national security. This practice, though legal, skirts the traditional avenues of data collection, highlighting a concerning trend of privacy erosion in the digital age. #GovernmentSurveillance #PrivacyConcerns #TechEthics 📱 The misconception that data sold to the government is collected with full consent and remains anonymous is debunked. In reality, privacy policies seldom mention government acquisition, and the so-called anonymization fails to prevent re-identification, posing a real threat to personal privacy. #DataAnonymity #Consent #PrivacyPolicy 👥 Internal government discussions reflect a tension between leveraging available data for public safety and adhering to America's privacy values. This balance challenges officials to justify the use of commercially available data for national security, revealing a complex interplay between privacy rights and government interests. #NationalSecurity #PublicSafety #PrivacyDebate 🌐 The concept of "gray data," or the incidental data collected from our increasing array of connected devices, opens new frontiers for surveillance. From Bluetooth signals to car tire pressure monitors, this data provides a rich source for tracking, further blurring the lines of privacy in the digital age. #ConnectedDevices #SurveillanceTechnology #GrayData 🔒 The implications of widespread surveillance touch on fundamental civil liberties, with potential impacts on issues like abortion access in a post-Roe v. Wade landscape. The omnipresent digital footprint makes it nearly impossible to maintain privacy or anonymity, challenging the very fabric of a free society. #CivilLiberties #AbortionAccess #DigitalFootprint These revelations call for a critical examination of the balance between technological advancement, government surveillance, and individual privacy rights, urging a reevaluation of the boundaries of legal data acquisition and use.