#Homomorphic Encryption

[raybittechnologies]

Enhancing Privacy and Security in Data Collaboration with PETs

In today’s digital world, data collaboration is a need for innovation, business growth, and research. However, while organizations and industries are more and more sharing data to create AI models or conducting market research, the risk of leakage of sensitive information increases. Today, privacy and security concerns are much higher due to increasing data breaches and strict privacy regulations. To address these challenges, PETs have emerged as an important technological tool for securing data collaboration to protect individual and organizational privacy.

What is Privacy Enhancing Technologies (PETs)?

Privacy Enhancing Technologies (PETs) are a range of tools and techniques that work towards the protection of an individual’s and organizations’ privacy when they need to share information to achieve some form of cooperation or collaboration. By applying cryptographic and statistical techniques, PETs prevent any unauthorized access to sensitive information without hindering its usefulness for analysis and decision-making.

These technologies are critical in today’s data-driven world, where organizations need to collaborate across borders and sectors, yet still have to abide by strict data privacy laws such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA).

How PETs Enhance Privacy and Security in Data Collaboration

Secure data collaboration requires that organizations can share insights and knowledge without exposing raw or sensitive data. PETs facilitate this by employing a variety of privacy-preserving techniques:

1. Homomorphic Encryption: Computing on Encrypted Data Homomorphic encryption would allow computations to be done on data that is encrypted, ensuring that data can stay encrypted while undergoing processing and analysis. At no point in the process, therefore, is sensitive data exposed, even when data is being analyzed or manipulated.

Example: In healthcare, the organizations can collaborate on medical research using encrypted patient data so sensitive information such as medical history or personal identifiers are not disclosed.

2. Federated Learning: Collaborative AI Without Data Sharing This facilitates decentralized data without the raw need to share between parties while training machine learning models together. The model will learn locally on each of these datasets, and it is only the updates from this model that are being shared, hence preserving privacy within the underlying data.

Example: Financial institutions can develop algorithms that detect fraud without exposing personal banking data of customers so as to maintain data privacy, with a reduced risk of breaching them.

3. Differential Privacy: Safeguarding Individual Data in Aggregated Insights Differential privacy introduces statistical noise in datasets to preserve the presence of individual data points in a way that it’s still possible to get informative insights. In other words, the addition or subtraction of a single data point should not greatly alter the final outcome.

Example: A tech company may find user behavior to enhance their product features without letting any person know about the activities related to his/her personal behavior.

4. Secure Multi-Party Computation (SMPC): Joint Computation Without Revealing Data SMPC is a type of computation that allows two or more parties to compute a joint function without any party revealing its individual data inputs. Each party holds its data private, and the computation is performed in a way that no party gets access to the other’s data.

Example: Two pharmaceutical companies can jointly analyze the results of clinical trials to discover new drug combinations without exposing proprietary data or patient information.

5. Zero-Knowledge Proofs (ZKP): Verifying Information Without Disclosing Data A zero-knowledge proof is a method by which one party can prove to another that a statement is true without revealing the underlying data. ZKPs support verification without exposing sensitive information or confidential information.

Example: a financial institution can prove its client’s creditworthiness without revealing any details on the client’s transactions and financial history.

The Benefits of PETs for Secure Data Collaboration

PETs offer several key advantages for organizations involved in data collaboration:

1. Stronger Privacy Preserving PETs make sure that data is not shared with anyone and still get analyzed in a collaborative way. Even through homomorphic encryption, federated learning, or any other cryptographic techniques, information could be kept safe from unprivileged accesses.

2. Complies with Regulations With increasing stringent data privacy regulations, organizations must be sure of adhering to laws like GDPR and CCPA. PETs enable organizations to fulfill such legal requirements, ensuring individual privacy and making sure that data is processed compliantly.

3. Data Security in Collaborative Environments In whatever form-whether it be a joint research project or a cross-organizational partnership-PETs help safeguard the data exchanges by preventing access to the data during collaboration. This is particularly essential for industries like healthcare, finance, and government, which are extremely sensitive to the fallout from breaches of data.

4. Trust and Transparency This would help organizations create trust among customers and partners by indicating that they have a sense of security and privacy in their information. This would positively influence brand reputation and promote long-term relationships with the stakeholders involved.

5. Innovation Not at the Expense of Privacy PETs allow for the innovation of using data in business without compromising privacy. Organizations are then able to harness the value of shared data without jeopardizing customers’ or employees’ sensitive data.

Real-World Applications of PETs in Data Collaboration

1. Healthcare: PETs allow hospitals and medical research institutions to collaborate on clinical trials and health data analysis without exposing patient records, thus maintaining privacy while advancing medicine.

2.Financial Services: Banks and financial institutions use PETs to detect fraudulent activities and share risk assessments without compromising customer information.

3. Government: Government agencies use PETs to share data across borders for policymaking or disaster response efforts, but with the assurance that citizen information is protected.

4. Retail and E-Commerce: Companies can share consumer behavior data across brands to enhance product offerings while respecting consumer privacy.

Challenges and Future of PETs in Data Collaboration

While PETs present robust privacy protection, deploying them is not without its challenges. Implementing these technologies is quite difficult for some organizations because their complexity, computational intensity, and the need for special technical expertise make them challenging. Another challenge is ensuring interoperability across different PETs and platforms.

However, as concerns over privacy grow, so will the demand for PETs. The future of secure data collaboration is going to be driven by innovations in quantum-safe encryption, AI-driven privacy solutions, and blockchain-based data sharing models, making PETs more powerful and accessible.

Conclusion

Privacy Enhancing Technologies (PETs) are playing a critical role in changing the way organizations share data. PETs will help organizations unlock the full value of their data while at the same time mitigating the risks associated with privacy breaches through secure and privacy-preserving data sharing. In this respect, PETs will be critical in ensuring that data collaboration remains secure, compliant, and privacy focused as the digital landscape continues to evolve.

[sageuniversitybpl]

Mathematics in Cryptography: Securing the Digital World

[marithlizard]

Oh _lovely_. Everyone go turn this off:

Enhanced Visual Search in Photos allows you to search for photos using landmarks or points of interest. Your device privately matches places in your photos to a global index Apple maintains on our servers. We apply homomorphic encryption and differential privacy, and use an OHTTP relay that hides [your] IP address. This prevents Apple from learning about the information in your photos. You can turn off Enhanced Visual Search at any time on your iOS or iPadOS device by going to Settings > Apps > Photos. On Mac, open Photos and go to Settings > General.

[wuggen]

God, homomorphic encryption is so fucking cool

[cyber-sec]

Apple Unveils Homomorphic Encryption Package for Secure Cloud Computing

Source: https://hackread.com/apple-homomorphic-encryption-secure-cloud-computing/

More info: https://www.swift.org/blog/announcing-swift-homomorphic-encryption/

Repo: https://github.com/apple/swift-homomorphic-encryption

[hackernewsrobot]

Swift Homomorphic Encryption

https://www.swift.org/blog/announcing-swift-homomorphic-encryption/

[harrelltut]

WELCOME BACK HOME IMMORTAL [HIM] U.S. MILITARY KING SOLOMON-MICHAEL HARRELL, JR.™

i.b.monk [ibm.com] mode [i’m] tech [IT] steelecartel.com @ quantumharrelltech.ca.gov

quantumharrelltelecom.tech sky military universe [mu] outside mars’ [mom’s] golden water firmament dome over earth [qi]

Bonjour Alice

Eye Quantum [EQ] Computing Intel Architect [CIA] Technocrat 1968-michaelharrelljr.com… 1st 9 Ether ALUHUM ANUNNAGI TECHNOCRAT of TIAMAT'S [AT&T'S] METADATA @ quantumharrelltelecom.tech

Eye Quantum [EQ] Computing Intel Architect [CIA] Technocrat 1968-michaelharrelljr.com… 1st 9 Ether ALUHUM ANUNNAGI TECHNOCRAT of TIAMAT [AT&T] on Architecting Fully Homomorphic Encryption-based Computing Systems @ quantumharrelltelecom.tech

Eye Quantum [EQ] Computing Intel Architect [CIA] Technocrat 1968-michaelharrelljr.com… 1st 9 Ether ALUHUM ANUNNAGI QUADRILLIONAIRE UNDER STATE [U.S.] MILITARY.gov CONTRACT @ quantumharrelltelecom.tech

Eye Quantum [EQ] Computing Intel Architect [CIA] Technocrat 1968-michaelharrelljr.com… 1st 9 Ether ALUHUM ANUNNAGI TECHNOCRAT of TIAMAT'S [AT&T'S] FINANCIAL CRYPTOGRAPHY AND DATA SECURITY @ quantumharrelltelecom.tech

Eye Quantum [EQ] Computing Intel Architect [CIA] Technocrat 1968-michaelharrelljr.com… 1st 9 Ether ALUHUM ANUNNAGI TECHNOCRAT of TIAMAT'S [AT&T'S] TRENDS IN DATA PROTECTION AND ENCRYPTION TECHNOLOGIES @ quantumharrelltelecom.tech

Eye Quantum [EQ] Computing Intel Architect [CIA] Technocrat 1968-michaelharrelljr.com… 1st 9 Ether ALUHUM ANUNNAGI TECHNOCRAT of TIAMAT'S [AT&T'S] COMPLEX INTELLIGENT SYSTEMS AND THEIR APPLICATIONS @ quantumharrelltelecom.tech

Eye Quantum [EQ] Computing Intel Architect [CIA] Technocrat 1968-michaelharrelljr.com… 1st 9 Ether ALUHUM ANUNNAGI TECHNOCRAT of TIAMAT'S [AT&T'S] 9 ETHER R.E. ENGINEERING SYSTEMS @ quantumharrelltelecom.tech

Eye Quantum [EQ] Computing Intel Architect [CIA] Technocrat 1968-michaelharrelljr.com… 1st 9 Ether ALUHUM ANUNNAGI TECHNOCRAT of TIAMAT'S [AT&T'S] COMPLEX INTELLIGENT AND SOFTWARE INTENSIVE SYSTEMS @ quantumharrelltelecom.tech

SEE?!?!?!

Eye Quantum [EQ] Computing Intel Architect [CIA] Technocrat 1968-michaelharrelljr.com… 1st 9 Ether ALUHUM ANUNNAGI TECHNOCRAT of TIAMAT [AT&T] @ quantumharrelltelecom.tech's Department of defense.gov 

© 1698-2223 quantumharrelltech.com - ALL The_Octagon_(Egypt) DotCom [D.C.] defense.gov Department Domain Communication [D.C.] Rights Reserved @ quantumharrelltech.ca.gov

[johnmargaretwrites]

The Ultimate Guide to Secure AI Model Training: Best Practices and Tools

As artificial intelligence (AI) continues to revolutionize industries, ensuring the security of AI model training has become paramount. With the vast amounts of data involved in AI model training, the risks of data breaches, intellectual property theft, and adversarial attacks are real and growing. In this guide, we’ll explore the best practices for securing AI model training and the tools that can safeguard your models and data. Additionally, we'll highlight how OpenLedger, a leading decentralized platform, enhances security in AI training processes.

Why AI Model Security Matters

AI models are built by training algorithms on large datasets, often containing sensitive or proprietary information. If these models are compromised, they can be manipulated to perform malicious tasks, resulting in reputational damage, legal repercussions, and financial loss. AI model security is crucial in protecting:

Data Privacy: Protecting personal or confidential data used in training AI models.

Model Integrity: Ensuring that the AI models are not tampered with, either during training or after deployment.

Adversarial Attacks: Safeguarding against attempts to manipulate AI models through subtle inputs designed to exploit vulnerabilities.

Best Practices for Secure AI Model Training

Data Encryption Encryption is the cornerstone of data security. Ensuring that all data used for AI model training is encrypted, both in transit and at rest, helps protect it from unauthorized access. Use state-of-the-art encryption protocols such as AES-256 to encrypt sensitive data.

Data Anonymization For datasets containing personal information, anonymization techniques such as differential privacy can be used to remove personally identifiable information. This reduces the risk of data exposure while ensuring the utility of the data for training purposes.

Access Control Limiting access to the training data and models to authorized personnel is a fundamental security practice. Implement role-based access control (RBAC) and multi-factor authentication (MFA) to ensure that only the right people have access to sensitive resources.

Secure Model Storage Storing AI models securely is as important as securing the data used to train them. Use secure cloud storage or on-premise solutions with strong encryption to protect trained models from unauthorized tampering.

Regular Audits and Monitoring Conduct regular security audits and continuous monitoring to detect any unusual activity. This could include monitoring model performance and behavior for signs of adversarial manipulation or degradation.

Federated Learning Federated learning is a decentralized approach to training AI models where the training data never leaves its original location. Instead, model updates are aggregated centrally, which significantly enhances data privacy and security. This decentralized approach also reduces the risk of data breaches.

Tools for Securing AI Model Training

Homomorphic Encryption Homomorphic encryption allows computations to be performed on encrypted data without decrypting it. This technology ensures that data privacy is maintained while still enabling the AI model to learn from it.

Trusted Execution Environments (TEEs) TEEs are isolated environments where AI models can be trained securely, preventing unauthorized access to the model’s data and computations. TEEs ensure that both data and code remain protected throughout the training process.

AI Model Watermarking To protect intellectual property, AI model watermarking embeds unique identifiers into models, which helps detect and prove ownership in the event of theft or unauthorized use.

AI Security Frameworks AI security frameworks like TensorFlow Privacy and PySyft are designed to implement privacy-preserving techniques such as differential privacy, federated learning, and secure multi-party computation, which are vital for securing model training.

OpenLedger’s Role in Securing AI Model Training

OpenLedger, a decentralized platform that leverages blockchain technology, offers robust security advantages for AI model training. Here’s how OpenLedger enhances the security and integrity of the AI training process:

Immutable Data Logs OpenLedger’s blockchain-powered system provides an immutable ledger that logs every interaction and transaction during the AI model training process. This ensures that the training process is transparent and traceable, allowing teams to verify that no unauthorized changes or tampering have occurred.

Decentralized Access Control OpenLedger’s decentralized nature means that access to data and models is not controlled by a single entity. Instead, it uses cryptographic techniques to ensure that only authorized users can access training data, providing a higher level of trust and security.

Secure Data Sharing Through OpenLedger, organizations can share data securely across decentralized networks without exposing it to centralized risks. This is particularly valuable in collaborative AI model training, where different parties need to contribute data without compromising privacy or security.

Blockchain-Based Federated Learning OpenLedger supports federated learning, which allows AI models to be trained without moving sensitive data from its origin. Federated learning on OpenLedger ensures that the data remains securely stored and only updates are shared, reducing the risk of data breaches.

Auditability and Accountability Using OpenLedger’s platform, every AI model training session is traceable, providing an audit trail of all activities. In case of a security breach or suspicion of malicious activity, these logs can be invaluable in investigating and mitigating the impact.

Tokenized Incentives for Security OpenLedger’s use of tokenization allows incentivization for good security practices. For instance, participants in a decentralized AI model training process could earn tokens for contributing secure data or implementing best practices, creating a self-reinforcing cycle of security.

The Future of Secure AI Model Training

As AI continues to evolve, the need for robust security measures in model training will only intensify. Using decentralized platforms like OpenLedger provides a more secure, transparent, and efficient environment for training AI models while safeguarding sensitive data and intellectual property.

By adopting the best practices and tools mentioned above and leveraging the decentralized advantages of OpenLedger, businesses can build AI systems that are not only innovative but also secure, trustworthy, and resilient against evolving security threats. Embracing these practices ensures that AI model training can continue to advance without compromising security, ultimately helping organizations maintain a competitive edge while minimizing risk.

[usaii]

Federated Learning: Powering AI With Innovation and Privacy | USAII®

Understand what the future of Advanced AI holds concerning astounding innovations and nuanced privacy. This read equips you with Federated Learning to the core!

Read more: https://shorturl.at/Kbiyf

Federated Learning, homomorphic encryption, Secure Multi-Party Computation (SMPC), AI model, IoT revolution, edge computing, federated transfer learning

[airionvez58]

YOUR GATEWAY TO CUTTING-EDGE TRENDS/Airionvez.com

1. ROBOTICS:

Robotics is going to become mainstream. While there has been a lot of hype around robotics for some time, several hardware and software technologies are only now becoming mature enough. Agriculture is one of the first industries that will benefit.

2.BRAIN-COMPUTER INTERFACES :

Technologies that allow direct communication between the brain and external devices are advancing rapidly. These interfaces could have profound implications for medical treatments and human-computer interaction.

3.GENERATIVE- AI:

Tools like ChatGPT have popularized generative AI, which can create text, images, videos, and more from prompts. This technology is reshaping industries and driving significant investments from major tech companies.

4. HOMOMORPHIC ENCRYPTION:

Homomorphic encryption is an emerging technology that opens up possibilities that most people would view as unachievable. It can allow two parties to collaborate and compute something without revealing the secret data. This could open up countless opportunities that today are impossible or restricted.

5.CONVERSATIONAL AI:

It’s only a matter of time before Google or Amazon releases a version of their software that will be easily able to sustain a conversation with humans. And that’s when we are going to see massive adoption. While casually chatting with Alexa we will be able to perform tasks ranging from following basic food recipes to planning.

6. AUGMENTED REALITY:

Augmented reality has made a lot of progress over the last few years and may finally be ready for widespread adoption. In a warehouse fulfillment scenario, an AR display could easily guide employees to the location of products, ensure the correct items have been selected and even help efficiently pack the items for delivery.

7. PASSWORD LESS AUTHENTICATION:

The computer password was created in 1960 and is still at the forefront of authentication. It is also the leading cause of data breaches. Soon the password as we know it will cease to exist, and we’ll see widespread adoption of passwordless authentication. This is a big step toward catching authentication up with the technology that we know today.

8. AR/VR In Real Estate And Construction:

The use of artificial reality and virtual reality in the real estate and construction sectors will be a game-changer. Before you invest in fit-outs or construction, you can preview the changes with this technology and avoid incurring costs after changes are made.

https://airionvez.com

# airionvez, airionvez.com ,airionvez

[3acesnews]

NVIDIA Enhances Data Privacy with Homomorphic Encryption for Federated XGBoost

[meeranjaz]

Exploring the Next Frontier of Data Science: Trends and Innovations Shaping the Future

Data science has evolved rapidly over the past decade, transforming industries and reshaping how we interact with data. As the field continues to grow, new trends and innovations are emerging that promise to further revolutionize how data is used to make decisions, drive business growth, and solve global challenges. In this blog, we will explore the future of data science and discuss the key trends and technologies that are likely to shape its trajectory in the coming years.

Key Trends in the Future of Data science:

Artificial Intelligence (AI) and Machine Learning Advancements The integration of artificial intelligence (AI) and machine learning (ML) with data science is one of the most significant trends shaping the future of the field. AI algorithms are becoming more sophisticated, capable of automating complex tasks and improving decision-making in real-time. Machine learning models are expected to become more interpretable and transparent, allowing businesses and researchers to better understand how predictions are made. As AI continues to improve, its role in automating data analysis, pattern recognition, and decision-making will become even more profound, enabling more intelligent systems and solutions across industries.

Explainable AI (XAI) As machine learning models become more complex, understanding how these models make decisions has become a crucial issue. Explainable AI (XAI) refers to the development of models that can provide human-understandable explanations for their decisions and predictions. This is particularly important in fields like healthcare, finance, and law, where decision transparency is critical. The future of data science will likely see a rise in demand for explainable AI, which will help increase trust in automated systems and enable better collaboration between humans and machines.

Edge Computing and Data Processing Edge computing is poised to change how data is processed and analyzed in real-time. With the rise of the Internet of Things (IoT) devices and the increasing volume of data being generated at the "edge" of networks (e.g., on smartphones, wearables, and sensors), there is a growing need to process data closer to where it is collected, rather than sending it to centralized cloud servers. Edge computing enables faster data processing, reduced latency, and more efficient use of bandwidth. In the future, data science will increasingly rely on edge computing for applications like autonomous vehicles, smart cities, and real-time healthcare monitoring.

Automated Machine Learning (AutoML) Automated Machine Learning (AutoML) is another trend that will shape the future of data science. AutoML tools are designed to automate the process of building machine learning models, making it easier for non-experts to develop predictive models without needing deep technical knowledge. These tools automate tasks such as feature selection, hyperparameter tuning, and model evaluation, significantly reducing the time and expertise required to build machine learning models. As AutoML tools become more advanced, they will democratize access to data science and enable more organizations to leverage the power of machine learning.

Data Privacy and Security Enhancements As data collection and analysis grow, so do concerns over privacy and security. Data breaches and misuse of personal information have raised significant concerns, especially with regulations like GDPR and CCPA in place. In the future, data science will focus more on privacy-preserving techniques such as federated learning, differential privacy, and homomorphic encryption. These technologies allow data models to be trained and analyzed without exposing sensitive information, ensuring that data privacy is maintained while still leveraging the power of data for analysis and decision-making.

Quantum Computing and Data Science Quantum computing, while still in its early stages, has the potential to revolutionize data science. Quantum computers can perform calculations that would be impossible or take an impractically long time on classical computers. For example, quantum algorithms could dramatically accelerate the processing of complex datasets, optimize machine learning models, and solve problems that currently require huge amounts of computational power. Although quantum computing is still in its infancy, it holds promise for making significant strides in fields like drug discovery, financial modeling, and cryptography, all of which rely heavily on data science.

Emerging Technologies Impacting Data Science:

Natural Language Processing (NLP) and Language Models Natural Language Processing (NLP) has made huge strides in recent years, with language models like GPT-4 and BERT showing remarkable capabilities in understanding and generating human language. In the future, NLP will become even more integral to data science, allowing machines to understand and process unstructured data (like text, speech, and images) more effectively. Applications of NLP include sentiment analysis, chatbots, customer service automation, and language translation. As these technologies advance, businesses will be able to gain deeper insights from textual data, enabling more personalized customer experiences and improved decision-making.

Data Democratization and Citizen Data Science Data democratization refers to the movement toward making data and data analysis tools accessible to a broader range of people, not just data scientists. In the future, more organizations will embrace citizen data science, where employees with little to no formal data science training can use tools to analyze data and generate insights. This trend is driven by the growing availability of user-friendly tools, AutoML platforms, and self-service BI dashboards. The future of data science will see a shift toward empowering more people to participate in data-driven decision-making, thus accelerating innovation and making data science more inclusive.

Augmented Analytics Augmented analytics involves the use of AI, machine learning, and natural language processing to enhance data analysis and decision-making processes. It helps automate data preparation, analysis, and reporting, allowing businesses to generate insights more quickly and accurately. In the future, augmented analytics will play a major role in improving business intelligence platforms, making them smarter and more efficient. By using augmented analytics, organizations will be able to harness the full potential of their data, uncover hidden patterns, and make better, more informed decisions.

Challenges to Address in the Future of Data science:

Bias in Data and Algorithms One of the most significant challenges that data science will face in the future is ensuring fairness and mitigating bias in data and algorithms. Data models are often trained on historical data, which can carry forward past biases, leading to unfair or discriminatory outcomes. As data science continues to grow, it will be crucial to develop methods to identify and address bias in data and algorithms to ensure that machine learning models make fair and ethical decisions.

Ethical Use of Data With the increasing amount of personal and sensitive data being collected, ethical considerations in data science will become even more important. Questions around data ownership, consent, and transparency will need to be addressed to ensure that data is used responsibly. As the field of data science evolves, there will be a growing emphasis on establishing clear ethical guidelines and frameworks to guide the responsible use of data.

Conclusion: The future of Data science is full of exciting possibilities, with advancements in AI, machine learning, quantum computing, and other emerging technologies set to reshape the landscape. As data science continues to evolve, it will play an even more central role in solving complex global challenges, driving business innovation, and enhancing our daily lives. While challenges such as data privacy, ethical concerns, and bias remain, the future of data science holds enormous potential for positive change. Organizations and individuals who embrace these innovations will be well-positioned to thrive in an increasingly data-driven world.

[atomiqx-technologies-co]

Israel

Our research covers the entire range of security solutions, including: data and AI security, privacy, cloud security, threat management, attack simulation, privacy preserving analytics, fully homomorphic encryption (FHE), blockchain, and central bank digital currency. Source