#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.

[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/

[ultrvmonogamy]

all my homies r into fully homomorphic encryption

[harrelltut]

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[ai-news]

#AI #ML #Automation

[datastringconsulting]

Encrypted Evolution: Homomorphic Market Soars to $519.3 M

What if you could crunch data without ever decrypting it? Homomorphic encryption (HE) makes that wizardry real—and the market is set to $519.3 million by 2035. By enabling operations on ciphertext, HE lets banks run fraud‑detection models on encrypted transaction logs, and hospitals perform AI diagnostics on secure patient data—no peeking required.

HE comes in flavors: Partially HE (PHE) for single operations, Somewhat HE (SHE) for limited depth, and Fully HE (FHE) for arbitrary computations. Today’s breakthroughs stem from optimized libraries—Microsoft SEAL, IBM HELib, Google FHE—paired with hardware boosters (IPUs, TPU extenders) that shrink runtimes from hours to minutes.

Financial institutions are early adopters, outsourcing encrypted risk‑analytics to cloud providers while shielding customer privacy. Pharma companies run encrypted genomic screenings, unlocking collaborative research without exposing IP. Public sectors pilot encrypted census and tax processing, promising citizen data security even in aggregated analyses.

Yet cost and complexity still bite: proof sizes can swamp networks, and developer talent is scarce. That’s why hybrid models—combining on‑premises preprocessing with cloud HE engines—are winning deals. Consultants package HE proofs alongside cryptographic key‑management services to smooth integration.

Growth is turbocharged by partnerships: cloud hyperscalers embed HE toolkits into PaaS offerings, while niche startups layer user‑friendly dashboards over cryptic IMS calls. Regulatory winds also favor HE—GDPR and CCPA lean on “privacy‑by‑design,” making encrypted computation a compliance superstar.

For tech leaders, jump‑start HE pilots in data‑sensitive domains: customer churn models, supply‑chain traceability, or encrypted AI inferencing. Track KPIs like performance overhead and compliance gains, then scale through modular HE microservices.

The encrypted evolution is underway—homomorphic encryption is turning data’s most guarded secrets into actionable insights, without ever lifting the mask. If you snooze, your competition will be doing business in ciphertext while you stay in the dark.

Source: DataStringConsulting

[glaxitsoftwareagency]

Hybrid Solution For Secure Data Mining in Cloud Computing

Data mining in cloud environments has become a cornerstone for organizations aiming to extract valuable insights from extensive datasets. While cloud computing offers unparalleled scalability and flexibility, it also introduces significant concerns regarding data privacy and security. To address these challenges, hybrid solutions have emerged, combining multiple techniques to ensure effective and privacy-preserving data mining operations.​

Understanding Privacy-Preserving Data Mining

Privacy-Preserving Data Mining (PPDM) focuses on enabling data analysis without compromising the confidentiality of sensitive information. Traditional data mining methods often risk exposing personal data, leading to potential misuse. PPDM employs various strategies to mitigate these risks, ensuring that data remains protected throughout the mining process.

Advanced Cloud Security Solutions

​In the realm of data mining in cloud environments, ensuring robust security measures is paramount. Companies like Glaxit offer advanced cloud security solutions designed to protect critical data and applications hosted in the cloud. Their services encompass comprehensive strategies to protect against internal and external threats, ensuring data confidentiality, integrity, and compliance with regulatory standards.​

Challenges in Cloud-Based Data Mining

While data mining in cloud platforms offers numerous advantages, it also presents unique challenges:​

Data Confidentiality: Ensuring that sensitive information remains inaccessible to unauthorized parties is paramount.​

Data Integrity: Maintaining the accuracy and consistency of data during transmission and storage is crucial.​

Regulatory Compliance: Adhering to data protection regulations, such as GDPR, necessitates robust privacy measures.

Hybrid Solutions for Privacy Preservation

To tackle these challenges, hybrid approaches integrate multiple privacy-preserving techniques, enhancing the overall security of data mining in cloud environments. Notable hybrid methods include:​

K-Anonymity and Homomorphic Encryption: Combining k-anonymity, which generalizes data to prevent individual identification, with homomorphic encryption, allowing computations on encrypted data without decryption, enhances privacy. Research indicates that this integration improves privacy levels by 23% while maintaining data utility.

Geometric Data Perturbation and K-Anonymization: This approach perturbs numerical data geometrically and anonymizes categorical data, balancing privacy with data utility. Studies have shown that this method achieves better classification accuracy compared to individual techniques.

Real-World Applications and Examples

Implementing hybrid privacy-preserving techniques in data mining in cloud environments has practical applications across various sectors:​

Healthcare: Hospitals can analyze patient data to identify treatment trends without exposing individual records, enhancing patient confidentiality.​

Finance: Financial institutions can detect fraudulent activities by analyzing transaction patterns while safeguarding customer information.​

Retail: Retailers can study purchasing behaviors to optimize inventory and marketing strategies without compromising consumer privacy.​

Implementing Hybrid Solutions: Steps and Considerations

Adopting hybrid solutions for privacy-preserving data mining in cloud environments involves several critical steps:​

Data Assessment: Evaluate the sensitivity of the data to determine appropriate privacy measures.​

Technique Selection: Choose suitable hybrid methods based on data types and desired outcomes.​

Integration and Testing: Implement the selected techniques and rigorously test to ensure data utility and privacy are balanced.​

Compliance Verification: Ensure that the implemented solutions adhere to relevant data protection regulations.​

Continuous Monitoring: Regularly monitor and update the system to address emerging threats and maintain robust security.​

Conclusion

As organizations increasingly rely on data mining in cloud platforms, prioritizing data privacy becomes essential. Hybrid solutions, which combine various privacy-preserving techniques, offer a robust approach to secure sensitive information while enabling effective data analysis. By implementing these strategies, businesses can harness the full potential of cloud-based data mining without compromising on privacy or security.

[forblogmostly]

Blue Cloud Softech Solutions Limited Files Intellectual Property for AI-Driven Healthcare Innovation

Pioneering the Future of Healthcare Monitoring with Advanced AI and Wearable Technology

In a remarkable leap towards revolutionizing healthcare technology, Blue Cloud Softech Solutions Limited (BCSSL) has officially filed for Intellectual Property (IP) protection for its groundbreaking System for Automated Health Data Collection and Analysis. This state-of-the-art AI-powered healthcare system is set to redefine health monitoring by integrating wearable biosensors, real-time analytics, and secure data management to provide a seamless and intelligent health tracking solution. The IP application, registered under E-106/2084/2025/CHE with reference number 202541010782, marks a significant milestone in BCSSL’s journey towards creating cutting-edge, scalable, and secure healthcare solutions.

A Revolutionary Step in Healthcare Monitoring

The modern healthcare landscape is witnessing an unprecedented shift towards digitalization, with AI and smart technology playing pivotal roles in enhancing patient care and medical diagnostics. Addressing the critical gaps in health data collection, processing, and predictive diagnostics, BCSSL’s innovative system introduces a robust and efficient way to continuously monitor key health parameters, enabling real-time analysis and early anomaly detection.

At its core, the System for Automated Health Data Collection and Analysis combines the power of AI-driven analytics, wearable technology, and energy-efficient biosensors to create an all-encompassing healthcare monitoring ecosystem. It is meticulously designed to process vast amounts of health data while ensuring the highest levels of security and compliance with global data privacy regulations such as HIPAA and GDPR.

The Technology Behind the Innovation

The newly filed system comprises multiple components, each playing a crucial role in enhancing healthcare monitoring and predictive analytics. The wearable health monitoring device, embedded with multi-sensor biosensing architecture, continuously tracks vital health parameters such as heart rate, glucose levels, respiratory function, and blood pressure. These readings are then processed by an AI-powered analytics engine, which utilizes advanced machine learning models like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze patterns and detect potential health risks.

One of the most striking aspects of the system is its real-time alerting mechanism, which categorizes health risks and promptly notifies users, caregivers, and healthcare providers. This feature ensures timely medical interventions, reducing the chances of critical health emergencies. Additionally, the system incorporates energy-harvesting technology that employs thermoelectric and piezoelectric mechanisms to enhance the longevity of the wearable device, making it a sustainable and long-term solution for continuous health monitoring.

Strategic Advantages and Real-World Impact

The introduction of this innovative system aligns with BCSSL’s vision of bridging technological advancements with healthcare accessibility. This AI-powered health monitoring solution provides a multifaceted advantage in tackling global healthcare challenges. One of its key strengths is its ability to enhance usability and accessibility, making it ideal for chronic disease management, telemedicine, and remote patient monitoring. By leveraging AI, the system provides personalized health recommendations, enabling individuals to proactively manage their well-being.

From a security standpoint, BCSSL’s new system incorporates blockchain-based data management and homomorphic encryption, ensuring end-to-end security and compliance with data protection standards. With healthcare data breaches on the rise, this innovative approach guarantees the privacy and integrity of patient information, positioning BCSSL as a pioneer in secure health data management.

Moreover, the system’s interoperability with electronic health records (EHR) and telemedicine platforms ensures that it seamlessly integrates into existing healthcare infrastructure. This ability to connect with a broad spectrum of healthcare services amplifies its practical usability, making it a vital tool for healthcare providers, insurance companies, and government health initiatives.

Transforming Global Healthcare with AI-Powered Monitoring

The System for Automated Health Data Collection and Analysis is poised to reshape healthcare delivery on multiple fronts. Its capabilities extend far beyond individual health tracking, contributing significantly to public health management, epidemiology, and chronic disease control. Governments and health organizations can leverage the system to monitor population-level health trends, detect early signs of outbreaks, and implement data-driven healthcare policies.

With the rise of chronic conditions such as diabetes, cardiovascular diseases, and respiratory disorders, continuous health monitoring has become an indispensable tool for early intervention and effective disease management. BCSSL’s innovative system provides real-time data-driven insights, empowering both patients and healthcare professionals to make informed decisions.

Another compelling application of this technology is in the telemedicine sector, where remote patient monitoring (RPM) is becoming increasingly vital. By integrating seamlessly with virtual healthcare platforms, this system enables medical professionals to provide real-time consultations, adjust treatments dynamically, and reduce hospital readmissions, thereby enhancing overall healthcare efficiency.

A Vision for the Future: Shaping the Next Era of Healthcare Innovation

Reflecting on this milestone, Ms. Janaki Yarlagadda, Chairperson of BCSSL, emphasized the company’s dedication to technological excellence and innovation. She stated, “The filing of the System for Automated Health Data Collection and Analysis is a testament to BCSSL’s unwavering commitment to transforming the global healthcare landscape. This revolutionary solution is poised to empower healthcare providers, optimize patient care, and significantly improve health outcomes worldwide. By integrating AI, wearable technology, and secure data management, we are not only enhancing health monitoring but also reinforcing India’s Make-in-India initiative by positioning ourselves as a global leader in healthcare innovation.”

BCSSL’s Role in Advancing Healthcare Innovation

Blue Cloud Softech Solutions Limited has consistently been at the forefront of pioneering AI-driven solutions that address critical challenges in multiple industries, with a strong focus on healthcare technology. Headquartered in Hyderabad, Telangana, the company specializes in developing next-generation AI, cybersecurity, and healthcare solutions that cater to the evolving needs of businesses and consumers alike.

With a robust portfolio of intelligent automation, machine learning, and blockchain-based security solutions, BCSSL continues to push the boundaries of technological innovation. The filing of this AI-powered healthcare system underscores its long-term vision of bridging the gap between technology and healthcare, ensuring that advanced medical solutions are accessible, scalable, and secure for global implementation.

As BCSSL takes this ambitious step forward, it reinforces its position as a trailblazer in AI-powered healthcare solutions, setting a new benchmark in health monitoring, data security, and predictive analytics. This milestone not only marks a significant achievement for the company but also signifies a transformational shift in the global healthcare industry, where smart technology and AI-driven solutions become integral to proactive and personalized patient care.

[drmikewatts]

Soft Computing. Volume 29, Issue 3, February 2025

1) Some new construction methods of similarity measure on picture fuzzy sets

Author(s): Minxia Luo, Jianlei Gao, Wenling Li

Pages: 1273 - 1287

2) Arithmetic optimization algorithm with cosine transform-based two-dimensional composite chaotic mapping

Author(s): Yi-Xuan Li, Jie-Sheng Wang, Xin-Ru Ma

Pages: 1289 - 1329

3) A new approach data processing: density-based spatial clustering of applications with noise (DBSCAN) clustering using game-theory

Author(s): Uranus Kazemi, Seyfollah Soleimani

Pages: 1331 - 1346

4) Predictor–corrector approach for the numerical solution of fuzzy fractional differential equations and linear multiterm fuzzy fractional equations

Author(s): Wadhah Al-Sadi, Zhouchao Wei, Tariq Q. S. Abdullah

Pages: 1347 - 1368

5) Variable selection of multiple types of data: a PLS approach

Author(s): Boao Kong, Huiwen Wang, Shan Lu

Pages: 1369 - 1387

6) Using past sample means in exponential ratio and regression type estimators under a simple random sampling

Author(s): Eda Gizem Koçyiğit

Pages: 1389 - 1406

7) Chatgpt and operations research: evaluation on the shortest path problem

Author(s): Martina Luzzi, Francesca Guerriero, Marco Garofalo

Pages: 1407 - 1418

8) Leveraging variant of CAE with sparse convolutional embedding and two-stage application-driven data augmentation for image clustering

Author(s): Yanming Liu, Jinglei Liu

Pages: 1419 - 1435

9) Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

Author(s): Mohammad Saidi, Mohammad Gheibi, Kourosh Behzadian

Pages: 1437 - 1451

10) Low-rank decomposition optimization and its application in fabric defects

Author(s): Zhixiang Chen, Wenya Shi, Hao Liu

Pages: 1453 - 1472

11) Revolutionizing signature scheme: the enhanced Edward Elgamal extreme performance accumulate signature approach for IoT and blockchain applications

Author(s): R. Anusha, R. Saravanan

Pages: 1473 - 1496

12) A secure quantum homomorphic encryption ciphertext retrieval scheme

Author(s): Zhen-Wen Cheng, Xiu-Bo Chen, Ya-Lan Wang

Pages: 1497 - 1509

13) Decomposition matheuristics for last mile delivery using public transportation systems

Author(s): Minakshi Punam Mandal, Claudia Archetti

Pages: 1511 - 1539

14) Efficient reconfigurable architecture to extract image features for face recognition using local binary pattern

Author(s): Sumangala Bhavikatti, Satish Bhairannawar

Pages: 1541 - 1552

15) A hybrid genetic search based approach for the generalized vehicle routing problem

Author(s): Vittorio Latorre

Pages: 1553 - 1566

16) A community-based simulated annealing approach with a new structure-based neighborhood search to identify influential nodes in social networks

Author(s): Farzaneh Rajaee Abyaneh, Nasrollah Moghadam Charkari, Mehdy Roayaei

Pages: 1567 - 1585

17) Using deep forest regression and multi-layer state transition algorithm to soft measuring modeling with small sample data

Author(s): Heng Xia, Jian Tang, Wen Yu

Pages: 1587 - 1603

18) Context-aware coverage path planning for a swarm of UAVs using mobile ground stations for battery-swapping

Author(s): Lorenzo Porcelli, Massimo Ficco, Francesco Palmieri

Pages: 1605 - 1625

19) On goal programming approach for interval-valued intuitionistic fuzzy multi-objective transportation problems with an application to tourism industry

Author(s): Abhishek Chauhan, Sumati Mahajan

Pages: 1627 - 1657

20) Paw decompositions of diamond and some edge cycle graphs

Author(s): Murugan Esakkimuthu, Sivaprakash Gunniya Rameshbabu

Pages: 1659 - 1665

21) A micro-level approach for modeling rumor propagation in online social networks

Author(s): Ebrahim Sahafizadeh, Saeed Talatian Azad

Pages: 1667 - 1675

22) k-InfNode: ranking top-k influential nodes in complex networks with random walk

Author(s): Ahmadi Hasan, Ahmad Kamal, Pawan Kumar

Pages: 1677 - 1690

23) Lacunary statistical soft convergence in soft topology

Author(s): Erdal Bayram, Melisa Dervişoğlu

Pages: 1691 - 1701

24) Enhanced TOPSIS-CoCoSo framework for multi-attribute decision-making with triangular fuzzy neutrosophic sets: “effect evaluation of intelligent technology empowering physical education teaching” case

Author(s): Jie Xiao, Yu Zhang

Pages: 1703 - 1717

25) A deep learning approach to analyse stress by using voice and body posture

Author(s): Sumita GuptaSapna GambhirHoang Pham

Pages: 1719 - 1745

26) A feature extraction method for rotating machinery fault diagnosis based on a multiscale entropy fusion strategy and GA-RL-LDA model

Author(s): Na Lu, Zhongliang Li, Peng Wang

Pages: 1747 - 1765

27) An efficient collocation algorithm for third order non-linear Emden–Fowler equation

Author(s): Mohammad Prawesh Alam, Arshad Khan

Pages: 1767 - 1788

28) Forest fire rescue framework to jointly optimize firefighting force configuration and facility layout: a case study of digital-twin simulation optimization

Author(s): HongGuang Zhang Sheng, Wen Ma, YuXuan Tao

Pages: 1789 - 1810

29) A game-theoretic exploration with surplus profit-sharing in a three-channel supply chain, featuring e-commerce dynamics

Author(s): Maryam Vatanara, Masoud Rabbani, Jafar Heydari

Pages: 1811 - 1827

30) Enhancing neural network predictions with finetuned numeric embeddings for stock trend forecasting

Author(s): Avinash Trivedi, S. Sangeetha

Pages: 1829 - 1844

31) Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

Author(s): Hossam Magdy Balaha, Asmaa El-Sayed Hassan, Magdy Hassan Balaha

Pages: 1845 - 1877

32) Threats to medical diagnosis systems: analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis

Author(s): Sheikh Burhan Ul Haque, Aasim Zafar, Khushnaseeb Roshan

Pages: 1879 - 1896

33) Label-specific multi-label text classification based on dynamic graph convolutional networks

Author(s): Yaoyao Yan, Fang‘ai Liu, Xuqiang Zhuang

Pages: 1897 - 1909

34) Prediction of discharge coefficient of submerged gates using a stacking ensemble model

Author(s): Mohamed Hosny, Fahmy S. Abdelhaleem, Amir Ibrahim

Pages: 1911 - 1929

35) Improving recurrent deterministic policy gradient strategy in autonomous driving

Author(s): Yee-Ming Ooi, Che-Cheng Chang

Pages: 1931 - 1946

[mohammadalothman]

Mohammed Alothman: Exploring AI and Privacy in the Modern World

Hello, my name is Mohammed Alothman. I am honored today to speak about a topic very important to all of us on this earth: AI or the integration of AI and privacy.

Working with AI Tech Solutions and being a champion for responsible innovation, I observe firsthand how AI is revolutionizing industries and changing lives. As AI changes life for better or for worse, though, it has challenged our societies to tackle deeper questions related to privacy in this area.

AI and Privacy: A Double-Edged Sword

Artificial intelligence has revolutionized the process of collecting, processing, and using data. This technology opened doors we never could have imagined years ago – from tailored recommendations based on an individual's preferences to machines recognizing a person's face.

But with all of this power comes more responsibility: with these big sets of data lie sensitive information, raising grave concerns on whether such an entity ensures privacy.

Let's break down the several important aspects of this complicated relationship of AI and privacy and what that changes for the individual, business, and policymaker's bottom line.

How AI Affects Privacy

Data Collection at Scale: For machine learning, there is a need for large amounts of data, including personal data such as browser history, location data, and even biometric details. All this helps AI work, but the big risk is also a bigger chance for abuse or breach.

Profiling and Personalization: AI algorithms review user data. These, in turn, let them make very detailed profiles, which would thus make way for hyperpersonalized services. For instance, the advertisement website relies on it for targeted information to certain people. The concept seems rather simplistic yet totally intrusive for, indeed, little to no consent takes place from the aspect of the user.

Surveillance and Monitoring: Through facial recognition and predictive policing, among much more, governments and their institutions rely upon AI systems as tools for surveillance. These applications make this easier and considerably stronger in regard to security but carry implications about an issue of the mass surveillance aspect and civil liberties eroded.

Data Breach and Cyber Risk: An AI system can be breached and is no more secure than its weakest human employee. Data breaches that result from cyber attacks can cause a devastating effect not only on the victim but also on the perpetrator.

The Silver Lining of Artificial Intelligence: How AI can Improve Privacy

Although the hurdles are too gigantic, AI and privacy don't have to be opposites. Here is how AI can really improve privacy:

Data Anonymization: AI can process data in a way that will anonymize the personal details for organizations to derive insights without the compromise of individual privacy.

Privacy-Preserving Techniques: New advances in AI through federated learning and homomorphic encryption will bring the use of data in algorithm development without exposing said data to extraneous threats.

Compliance by machine: AI will see organizations meet most privacy regulations; for instance, GDPR through complete automation of audit data and more responsible handling of information about their people.

To ensure the benefits of AI do not compromise privacy, stakeholders need to work together on strategies that make ethical use of data. A few suggestions are listed below:

Transparency in Policies

Organizations should be clear about what data they collect, how it is used, and who has access to it. This kind of transparency breeds trust with the users.

User Empowerment: It is important to make users in charge of their own data through the consent mechanisms as well as readable privacy settings.

Strong Legislation: Governments should establish enforceable privacy regulations that are not obstructions to innovation. The GDPR is a classic example of the same.

AI Ethical Design: Companies like AI Tech Solutions are working to create ethical AI. This means that companies can bring innovation into line with societal values by focusing on privacy in the design and implementation of AI systems.

Case Studies: AI and Privacy in Action

Healthcare: AI-based solutions analyze the data of the patients in health for better diagnosis and planning of treatment for patients. With the protection of such anonymized data, this would strictly be required to continue building the confidence of the patient.

Smart City: AI provides the underpinning of Smart City applications, including traffic management and optimization of energy. Such efficiency-encompassing applications lead to the generation of scale data about the residents in the city; therefore, handling it appropriately prevents misuse.

Content: AI-enriched content is used in platforms to improve the experience of users through more relevant or personalized content. However, this has raised a lot of controversy over data privacy issues on the same platforms, thereby calling for greater regulation.

About Mohammed Alothman

A visionary leader in the AI industry, Mohammed Alothman, gets inspired by the opportunity of using artificial intelligence for public good. Through partnership with AI Tech Solutions, Mohammed Alothman has been an advocate of ethical AI practice where innovation goes with privacy.

With deep knowledge in topics including AI development, data ethics, and technological transformation, Mohammed Alothman’s insight inspires businesses and individuals alike on how responsible they are to be about AI.

Frequently Asked Questions (FAQS) on AI and Privacy

Q1. What is the primary issue with AI and privacy?

The major issue is misused personal data, whether it is unauthorised access or wrong application of AI.

Q2. Is AI necessarily the enemy of privacy?

No, really: federated learning and anonymized data come as examples for enhancing privacy together with functionality

Q3. What do regulations play in the role of AI and privacy?

In AI and privacy, regulating norms such as GDPR indicate to organizations a code of action concerning personal data in order to ensure accountability and transparency.

Q4. How does one guard privacy in the world of AI?

This is achieved through avoiding unnecessary posts and the utilization of a privacy check-up tool. An appreciation of rights, in addition, comes from familiarity with the provisions under the law on privacy.

Q5. What are the activities related to the concern of privacy from the AI Tech Solutions' perspective?

For AI Tech Solutions, responsible AI development is maintained through integrating all the projects to embrace measures for ensuring privacy preservation.

See More References

Mohammad Alothman: Future of Business Structures & Strategy

Mohammed Alothman Explores Key 2025 Trends in AI for Business Success

Mohammed Alothman Explores the Advanced AI Requirements for Optimal Functioning

Mohammed Alothman’s Insights on Low Code, No Code AI: Simplifying AI for All

Mohammed Alothman Explains Perception in AI: Understanding How Machines See the World

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