#Homomorphic Encryption

[digitaltechsolutions]

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

[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.™

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[pradip23]

Homomorphic Encryption Market Outlook from 2024 to 2030|

Homomorphic Encryption Market Overview

Maximize Market Research is a Business Consultancy Firm that has published a detailed analysis of the “Homomorphic Encryption Market”. The report includes key business insights, demand analysis, pricing analysis, and competitive landscape. The report provides the current state of the Homomorphic Encryption market by thorough analysis, and projections are made up to 2030.

Homomorphic Encryption Market Scope and Methodology:

The global market landscape is thoroughly examined in the Homomorphic Encryption market study, which focuses on a number of factors including product categories, end-user demographics, and distribution channels. This analysis provides in-depth insights into the market dynamics, including the forces, obstacles, and market trends influencing the industry's growth trajectory. The study also offers geographic insights to identify important areas and related growth prospects. The research provides a comprehensive analysis of the competitive landscape, opportunities, and challenges in the Homomorphic Encryption industry in order to assist participants in making informed decisions and capitalizing on emerging market trends.

Get A Sample Copy of This Report :https://www.maximizemarketresearch.com/request-sample/189065/

Homomorphic Encryption Market Report reliability and legitimacy are ensured by the rigorous research technique used by Maximize Market Research Pvt Ltd.  Private database reviews, market surveys, and in-depth interviews with subject matter experts are all part of the methodology, which combines primary and secondary research methodologies. Primary research is a process that involves directly obtaining information on market dynamics from key players such as distributors, suppliers, manufacturers, and end consumers. Examining pertinent literature, industry reports, corporate websites, and regulatory papers in-depth is secondary research, which is done to support and corroborate the results of primary research. A detailed assessment of the market environment, motivating factors, and competitive landscape is produced by the study's rigorous methodology adoption.

Homomorphic Encryption Market Regional Insights

Proficiency in regional knowledge is vital to comprehend the intricate dynamics of the Homomorphic Encryption industry. Homomorphic Encryption# is the market that includes North America, Europe, Asia Pacific, Latin America, the Middle East, and Africa. The report provides an in-depth examination of the factors, market size, growth rate, and import and export activities in each region. The Regional Analysis part of the study displays the present status of the Homomorphic Encryption market in the countries it covers.

Homomorphic Encryption Market SegmentationSegments Covered:by ApplicationSecure Cloud Computing Secure Outsourcing Secure Data Sharing Secure Machine Learning Othersby Encryption LevelPartially Homomorphic Encryption (PHE) Fully Homomorphic Encryption (FHE)by End-User IndustryFinance Healthcare Telecommunications Government And Defence Others

Homomorphic Encryption Market Key Players: 

1. Microsoft (United States) 2. IBM (United States) 3. Google (United States) 4. Amazon Web Services (United States) 5. Postquantum (United States) 6. Cornami (United States) 7. Intel Corporation (United States) 8. Vaultree (United States) 9. ShieldIO (United States) 10. SiFive (United States) 11. MuSig (United States) 12. SecureKey Technologies (Canada)

Europe

13. NCipher (United Kingdom) 14. Cryptomathic (United Kingdom) 15. Zaiku Group LTD (United Kingdom) 16. Cosmian (France) 17. CryptoExperts (France) 18. id Quantique (Switzerland)

Asia Pacific

19. Nirvana Systems (India) 20. Zama (India) 21. Adhara (India) 22. Unbound (India) 23. Desilo (South Korea)

South America

24. Crypta Labs (Argentina) 25. Quantum Resistant Ledger (Brazil) 26. Cartesi (Brazil)

Middle East & Africa

27. CipherTrace (United Arab Emirates) 28. Cryptography Research (Israel

Key questions answered in the Homomorphic Encryption Market are:

What are the upcoming industry applications and trends for the Homomorphic Encryption Market?

What are the recent industry trends that can be implemented to generate additional revenue streams for the Homomorphic Encryption Market?

Who are the leading companies and what are their portfolios in Homomorphic Encryption Market?

What segments are covered in the Homomorphic Encryption Market?

Who are the key players in the Homomorphic Encryption market?

Which application holds the highest potential in the Homomorphic Encryption market?

What are the key challenges and opportunities in the Homomorphic Encryption market?

What is Homomorphic Encryption?

What was the Homomorphic Encryption market size in 2024?

What will be the CAGR at which the Homomorphic Encryption market will grow?

What is the growth rate of the Homomorphic Encryption Market?

Which are the factors expected to drive the Homomorphic Encryption market growth?

What are the different segments of the Homomorphic Encryption Market?

What growth strategies are the players considering to increase their presence in Homomorphic Encryption?

 Browse Full Report for detail:

Key Offerings:

Past Market Size and Competitive Landscape (2018 to 2022)

Past Pricing and price curve by region (2018 to 2022)

Market Size, Share, Size & Forecast by different segment | 2024−2030

Market Dynamics – Growth Drivers, Restraints, Opportunities, and Key Trends by Region

Market Segmentation – A detailed analysis by segment with their sub-segments and Region

Competitive Landscape – Profiles of selected key players by region from a strategic perspective

Competitive landscape – Market Leaders, Market Followers, Regional player

Competitive benchmarking of key players by region

PESTLE Analysis

PORTER’s analysis

Value chain and supply chain analysis

Legal Aspects of Business by Region

Lucrative business opportunities with SWOT analysis

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[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

[drmikewatts]

IEEE Transactions on Evolutionary Computation, Volume 28, Number 5, October 2024

1) Particle Swarm Optimization for Efficiently Evolving Deep Convolutional Neural Networks Using an Autoencoder-Based Encoding Strategy

Author(s:); Gonglin Yuan, Bin Wang, Bing Xue, Mengjie Zhang

Pages: 1190 - 1204

2) Conditional Generative Adversarial Network-Based Bilevel Evolutionary Multiobjective Optimization Algorithm

Author(s:); Weizhong Wang, Hai-Lin Liu

Pages: 1205 - 1219

3) Can Evolutionary Clustering Have Theoretical Guarantees?

Author(s:); Chao Qian

Pages: 1220 - 1234

4) Genetic Programming With Lexicase Selection for Large-Scale Dynamic Flexible Job Shop Scheduling

Author(s:); Meng Xu, Yi Mei, Fangfang Zhang, Mengjie Zhang

Pages: 1235 - 1249

5) Correlation-Based Dynamic Allocation Scheme of Fitness Evaluations for Constrained Evolutionary Optimization

Author(s:); Han Huang, Yueting Xu, Yi Xiang, Zhifeng Hao

Pages: 1250 - 1264

6) Robust Optimization Over Time: A Critical Review

Author(s:); Danial Yazdani, Mohammad Nabi Omidvar, Donya Yazdani, Jürgen Branke, Trung Thanh Nguyen, Amir H. Gandomi, Yaochu Jin, Xin Yao

Pages: 1265 - 1285

7) Balancing Objective Optimization and Constraint Satisfaction in Expensive Constrained Evolutionary Multiobjective Optimization

Author(s:); Zhenshou Song, Handing Wang, Bing Xue, Mengjie Zhang, Yaochu Jin

Pages: 1286 - 1300

8) Estimation of Distribution Algorithms in Machine Learning: A Survey

Author(s:); Pedro Larrañaga, Concha Bielza

Pages: 1301 - 1321

9) Multiobjective Optimization-Based Network Control Principles for Identifying Personalized Drug Targets With Cancer

Author(s:); Jing Liang, Zhuo Hu, Zong-Wei Li, Kangjia Qiao, Wei-Feng Guo

Pages: 1322 - 1335

10) Privacy-Enhanced Multitasking Particle Swarm Optimization Based on Homomorphic Encryption

Author(s:); Hao Li, Fanggao Wan, Maoguo Gong, A. K. Qin, Yue Wu, Lining Xing

Pages: 1336 - 1350

11) Machine Learning-Based Prediction of New Pareto-Optimal Solutions From Pseudo-Weights

Author(s:); Anirudh Suresh, Kalyanmoy Deb

Pages: 1351 - 1365

12) A Genetic Programming Approach With Building Block Evolving and Reusing to Image Classification

Author(s:); Ying Bi, Jing Liang, Bing Xue, Mengjie Zhang

Pages: 1366 - 1380

13) Evolutionary Dynamic Constrained Multiobjective Optimization: Test Suite and Algorithm

Author(s:); Guoyu Chen, Yinan Guo, Yong Wang, Jing Liang, Dunwei Gong, Shengxiang Yang

Pages: 1381 - 1395

14) A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments

Author(s:); Ke Li, Renzhi Chen, Xin Yao

Pages: 1396 - 1411

15) Interactively Learning Rough Strategies That Dynamically Satisfy Investor’s Preferences in Multiobjective Index Tracking

Author(s:); Julio Cezar Soares Silva, Adiel Teixeira de Almeida Filho

Pages: 1412 - 1426

16) A Self-Adaptive Collaborative Differential Evolution Algorithm for Solving Energy Resource Management Problems in Smart Grids

Author(s:); Haoxiang Qin, Wenlei Bai, Yi Xiang, Fangqing Liu, Yuyan Han, Ling Wang

Pages: 1427 - 1441

17) Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency for Many Objectives

Author(s:); Weijie Zheng, Benjamin Doerr

Pages: 1442 - 1454

18) Modular Multitree Genetic Programming for Evolutionary Feature Construction for Regression

Author(s:); Hengzhe Zhang, Qi Chen, Bing Xue, Wolfgang Banzhaf, Mengjie Zhang

Pages: 1455 - 1469

19) Regularity Evolution for Multiobjective Optimization

Author(s:); Shuai Wang, Aimin Zhou

Pages: 1470 - 1483

20) SR-Forest: A Genetic Programming-Based Heterogeneous Ensemble Learning Method

Author(s:); Hengzhe Zhang, Aimin Zhou, Qi Chen, Bing Xue, Mengjie Zhang

Pages: 1484 - 1498

21) Evolutionary Multitasking With Centralized Learning for Large-Scale Combinatorial Multiobjective Optimization

Author(s:); Yuxiao Huang, Wei Zhou, Yu Wang, Min Li, Liang Feng, Kay Chen Tan

Pages: 1499 - 1513

22) VSG3A2: A Genetic Algorithm-Based Virtual Sample Generation Approach Using Information Gain and Acceptance-Rejection Sampling

Author(s:); Hong Yu, Xuekang Fan, Guoyin Wang, Yongfang Xie

Pages: 1514 - 1528

23) A Dynamic-Niching-Based Pareto Domination for Multimodal Multiobjective Optimization

Author(s:); Juan Zou, Qi Deng, Yuan Liu, Xinjie Yang, Shengxiang Yang, Jinhua Zheng

Pages: 1529 - 1543

[rose-mason-talk]

Data Tokenization in 2025: A Comprehensive Guide

By 2025, is concerned about privacy and data security have reached levels that have never been seen before. Because of new privacy laws, more people using the cloud, and constant cyber threats, data tokenization has become a vital resource in this day and age. The remainder of this piece will talk about how data tokenization has changed over time, as well as its main uses, pros, and cons in the year 2025.

What is Data Tokenization?

It is possible for "tokenize" data, which means to replace sensitive information like credit card numbers, PII, or medical records with a non-sensitive version of that information. You can't use the token and figure out what it means without the data and the process of tokenization. It helps keep private data safe, which lowers the possibility of data breaches.

Key Components of Data Tokenization in 2025

a. Advanced Tokenization Algorithms

By 2025, the tokenization algorithms have advanced a great deal and are also more efficient. Nowadays, real-time data tokenization can be performed with modern algorithms on large amounts of information without considerable latency. Such algorithms are designed to maintain the usability of the original data as much as possible in the course of its maximum protection.

b. Tokenization in a Multicloud Environment

As enterprises are moving to multicloud architectures, tokenization systems today can allow integration on different clouds. Being able to tokenize and detokenize data within various environments enables organizations to secure their data and use different cloud providers for their elasticity and scalability.

c. Privacy-Enhanced Computation

proactively stay till 2025, tokenization will also use computation methods that protect privacy, like safe multiparty computation and homomorphic encryption. These methods make it possible to process private data in a cleaned-up or tokenized form that doesn't reveal the actual data. This makes it possible to share and analyze the data safely without compromising privacy.

How Tokenization Works in 2025

Step 1: Data Identification and Classification

Prior to any tokenization process, businesses first need to locate and categorize the confidential data within their systems. Due to emerging capabilities in machine learning and artificial intelligence, such data discovery systems are capable of detecting and classifying sensitive content present in both structured and unstructured databases without any human intervention.

Step 2: Tokenization Process

Following data has been classified, the private information is transformed to an unique resource which is generated by the tokenization system.like the credit card number "4111 1111 1111 1111" could be changed into a symbol like "ABCD1234XYZ5678." The original sensitive data is kept safely in a token vault that only those with permission can get to.During data usage, the tokenized format is employed, allowing operations to continue without exposing the original information. This ensures both the confidentiality of the data and its safe handling throughout the process.

Step 4: Detokenization (if needed)

There are a variety of applications where the tokenized data could be used, for instance, e-commerce, CRM systems, business intelligence tools, etc. Because these tokens do not carry sensitive information, the chances of data compromise, while the information is being processed or stored, is significantly mitigated for the organizations.

Benefits of Data Tokenization in 2025

a. Enhanced Data Privacy

The GDPR ( General Data Protection Regulation)in Europe and CCPA the California Consumer Privacy Act  are just two examples of rules and guidelines that require organizations to protect personal data. Other data protection policies are also on the way all over the world. In order to follow the rules, tokenization encrypts private data and lowers the chance that any personal identification information (PII) will be Shared.

b. Reduced Data Breach Risk

So when it comes to data theft, in most cases if not all of them the data that has been tokenized is of no value thus minimizes the risk or the effect of the outside threats. In most cases where an attacker possesses system information with a tokenized piece of data, the attacker cannot be able to disassemble or work out the data without the token storage.

c. Secure Data Analytics

In 2025, owing to the development of tokenization, businesses can now utilize and analyze sensitive information without venturing into privacy concerns. Take the case of the medical or financial industries; data tokenization allows predictive analytics and machine learning to be performed on sensitive data without breaking any data privacy laws.

d. Scalable Across Environments

Organizations can now execute tokenization within on-premises, hybrid, and multi-cloud environments. This flexibility extends even more for organizations migrating toward the cloud or extending their digital infrastructure as data protection becomes paramount.

Secure Your Data with Tokenization – Get Started Today with real world asset tokenization solutions

Use Cases for Tokenization in 2025

a. Financial Services

Tokenization has been usually employed by financial institutions to protect P-C-I and account numbers for years. More so, in 2025, the usage of banks, DeFi and cryptocurrencies propelled this entirely into a new dimension. Aids such as tokenization of identity and transaction provides a layer of security even for purpose of use wallets making it easier to operate under strict financial regulations.

b. Healthcare

The healthcare industry which handles very delicate patient information has embraced tokenization to safeguard medical records, insurance details, and billing information. Tokenization is used by healthcare institutions in the year 2025 for sharing anonymized medical data for the sake of research and collaboration without compromising on patient privacy.

c. E-commerce and Retail

Tokenization is a technique that e-commerce platforms have consistently employed to ensure the safety of customer financial data. In the year 2025, tokenization has been implemented to protect other forms of customer information such as loyalty schemes, delivery addresses, and purchase patterns. Retailers also embrace the use of tokenization to avoid breaching the GDPR and CCPA regulations when conducting worldwide business transactions.

d. IoT and Smart Devices

The Internet of Things (IoT) gets more popular. Tokenization saves the huge amounts of data that smart devices generate. By 2025, tokenization will make it impossible to get to private data like fingerprint data from smartwatches and even geographic information from autonomous vehicles.

Future Trends in Data Tokenization

a. Zero-Knowledge Proofs and Blockchain Integration

Tokenization is talked about more when talking about blockchain applications in 2025, especially when talking about decentralized banking and supply chain. With zero knowledge proofs (ZKP), users can show that a transaction or piece of data is real without giving away the private data itself. This way of doing things is a completely new way to keep info safe and private.

b. Quantum-Resistant Tokenization

Due to the possible advent of quantum computing, which has the capacity to cathartic current encryption protocols, quantum secure tokenization methods are in the process of being constructed. These methods make sure that the tokenized data will be safe even in the post-quantum world.

c. AI-Driven Tokenization

AI is fundamentally important in providing an automated approach to the tokenization process. By 2025, AI technologies will be able to operate in real time to locate sensitive information and campaign for its tokenization while adjusting itself to variations in the flow of information. This lightens the burden placed on humans and enhances the effectiveness of the mechanisms put in place to safeguard data integrity.

Conclusion

By the year 2025, data tokenization is no longer an emerging trend, but a holy grail in the modern strategies of securing data. As the cloud computing technologies, AI, and quantum resistant techniques improve, the tokenization technology today is not only more advanced but also secure and scalable to businesses. Regardless of the industry – be it finance, healthcare, retail, the Internet of Things, or others – organizations are utilizing tokenization to secure data, adhere to international laws, and minimize the chances of data losses.

With advancements in technology, whereby every day, new products and services reach the target market, it can be expected that even in 2025 and beyond, the practice of tokenization will be one of the foremost tools in the enhancement of privacy, security, and trust in this age that is highly reliant on data.

[cryptogirl2024]

Shiba Eternity Web3 Beta Launches for Shiboshi Holders

Shiba Eternity Web3 Beta is officially live, offering a fresh experience for NFT holders.

Major bugs were fixed & new features were added to enhance gameplay & user experience.

October brings tournaments and an in-game shop, boosting rewards and fun for players.

The much-anticipated Shiba Eternity Web3 Beta has finally launched, offering fans, Shiboshi Sheboshi, and Shiba Eternity NFT holders a new experience. Lucie, a Shiba Inu marketer, announced on X this Wednesday that the game is now live, and this is just the beginning of a new chapter for the Shib Army.

Complete Game Overhaul

Shib Games’ team has uploaded a new system after a long development period. The new version has better graphics, gameplay, and, above all, better time management. Shiba enthusiasts can now get more into the gaming world because the universe has expanded. This is a new version with new features and improvements added.

The Beta is available to all Shiboshi, Sheboshi, and Shiba Eternity NFT holders. Owners of these NFTs will be able to dive right into the game. The waiting is finally over, and the game is running fine. This is important for Shiba fans, who have been waiting for this release for a long time.

Major Bug Fixes

The majority of improvements in the new version aim to eliminate the defects present in the previous one. Some common issues, such as game freezing and animation problems, have been fixed, as has the login problem. A new feature has been added so players can still get rewards even if their opponent leaves the game. For this reason, a force-update function is applied to guarantee that all players are on the most current version.

Related:

October should bring even more interesting improvements. Competitive sessions are coming soon, where players will be able to show their talents and earn crypto prizes. The in-game shop will be available soon, where players can purchase items related to Shiba Inu. These new features will enhance the fun and value of the gameplay experience for the players.

Community-Driven Development

The Shiba Games team engaged its community to develop this upgrade. Ideas from players contributed to most of the design alterations. This means that the new version is what the Shib Army wanted. Thus, the team developed a game that meets their expectations and enhances the overall experience.

As the beta is now released, Shiboshi holders could experience the evolved universe of Shiba Eternity. The new version offers a new, improved, fun way to play the game. The Shib Army could finally have the game they have been waiting for, with more exciting features to be added soon.

[ai-news]

Federated Learning (FL) is a technique that allows Machine Learning models to be trained on decentralized data sources while preserving privacy. This method is especially helpful in industries like healthcare and finance, where privacy issues preven #AI #ML #Automation

[educationtech]

8 Emerging Cybersecurity Trends to Watch Out in 2024 - Arya College

The landscape of cybersecurity Trends is evolving rapidly, and IT professionals must stay informed about IT Security trends in 2024 to effectively protect organizations from emerging threats. As we move into 2024, several key trends are shaping the cybersecurity environment, driven by advancements in technology, changes in regulatory landscapes, and the evolving tactics of cybercriminals.

1. Integration of Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are becoming central to cybersecurity strategies. These technologies enhance threat detection and response capabilities by analyzing vast amounts of data in real time, identifying anomalies, and predicting potential security breaches. In 2024, organizations will increasingly rely on AI-driven security solutions to automate responses to threats, reducing the time it takes to mitigate attacks.

2. Proliferation of Ransomware Attacks

Ransomware remains a significant threat, with attackers increasingly targeting critical infrastructure and high-profile organizations. In 2023, ransomware affected 66% of organizations, highlighting the urgency for robust ransomware mitigation strategies. IT professionals must prioritize implementing multi-layered defenses, including regular backups, employee training, and proactive threat hunting to mitigate risks associated with ransomware attacks.

3. Supply Chain Security

Supply chain attacks are on the rise as cybercriminals exploit vulnerabilities within third-party vendors and partners. In 2024, organizations must enhance their supply chain risk management practices by conducting thorough vendor assessments and implementing stringent security protocols throughout their supply chain ecosystems.

4. Emphasis on Cloud Security

As organizations increasingly migrate to cloud environments, ensuring robust cloud security becomes paramount. This includes implementing strong identity and access management, continuous monitoring, and encryption of sensitive data stored in the cloud.

5. Adoption of Zero Trust Security Models

The Zero Trust security model, which operates on the principle of "never trust, always verify," is gaining traction as a fundamental approach to cybersecurity. In 2024, organizations will increasingly adopt zero-trust architectures, which require strict identity verification for every individual and device attempting to access resources, regardless of whether they are inside or outside the network perimeter.

6. Regulatory Compliance and Governance

With the increasing frequency of data breaches, regulatory compliance is becoming a critical focus for organizations. New regulations, such as the EU’s NIS2 Directive and changes by the SEC regarding material cybersecurity breaches, are prompting organizations to reassess their cybersecurity governance structures.

7. Enhanced Focus on Cybersecurity Training and Awareness

As cyber threats become more sophisticated, the need for comprehensive cybersecurity training for employees is more critical than ever. In 2024, organizations will invest in ongoing training programs to educate employees about recognizing phishing attempts, secure password practices, and safe internet usage. A well-informed workforce is a vital line of defense against emerging cyber threats, and organizations will prioritize fostering a culture of cybersecurity awareness.

8. Privacy-enhancing technologies (PETs)

With growing concerns about data privacy, Privacy-Enhancing Technologies (PETs) are emerging as essential tools for organizations. These technologies, such as homomorphic encryption and differential privacy, allow data to be processed while maintaining its confidentiality. In 2024, organizations will increasingly adopt PETs to comply with privacy regulations and protect sensitive information from unauthorized access. How is the cybersecurity market expected to consolidate in the coming year. The cybersecurity market is expected to see significant consolidation in the coming years, driven by several key factors: Fragmentation and Opportunity for Consolidation.

Conclusion

The cybersecurity landscape in 2024 presents both challenges and opportunities for IT professionals at Arya College of Engineering & IT, Jaipur. By staying informed about these trends and adopting proactive security measures, organizations can enhance their resilience against evolving cyber threats. Emphasizing AI and ML, strengthening supply chain security, adopting Zero Trust models, and ensuring regulatory compliance are all critical components of a robust cybersecurity strategy. As cyber threats continue to evolve, the Future of IT with IT professionals must remain vigilant and adaptable to safeguard their organizations effectively.