Privacy - Revision history

BPeat at 00:11, 17 August 2023

2023-08-17T00:11:25Z

BPeat at 00:11, 10 July 2023

2023-07-10T00:11:31Z

BPeat at 00:10, 10 July 2023

2023-07-10T00:10:10Z

BPeat at 19:32, 4 July 2023

2023-07-04T19:32:24Z

BPeat at 16:37, 30 June 2023

2023-06-30T16:37:47Z

BPeat at 20:06, 25 June 2023

2023-06-25T20:06:28Z

BPeat at 01:30, 18 May 2023

2023-05-18T01:30:00Z

BPeat at 12:06, 5 May 2023

2023-05-05T12:06:32Z

BPeat at 20:13, 20 April 2023

2023-04-20T20:13:33Z

BPeat at 19:38, 20 April 2023

2023-04-20T19:38:10Z

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 <b>Great Models with Great Privacy: Optimizing ML and AI Under GDPR with Sim Simeonov &Slater Victoroff
 </b><br>The General Data Protection Regulation (GDPR), which came into effect on May 25, 2018, establishes strict guidelines for managing personal and sensitive data, backed by stiff penalties. GDPR’s requirements have forced some companies to shut down services and others to flee the EU market altogether. GDPR’s goal to give consumers control over their data and, thus, increase consumer trust in the digital ecosystem is laudable.
-However, there is a growing feeling that GDPR has dampened innovation in machine learning & AI applied to personal and/or sensitive data. After all, ML & AI are hungry for rich, detailed data and sanitizing data to improve privacy typically involves redacting or fuzzing inputs, which multiple studies have shown can seriously affect model quality and predictive power. While this is technically true for some privacy-safe modeling techniques, it’s not true in general.  The root cause of the problem is two-fold. First, most data scientists have never learned how to produce great models with great privacy. Second, most companies lack the systems to make privacy-safe machine learning & AI easy. This talk will challenge the implicit assumption that more privacy means worse predictions. Using practical examples from production environments involving personal and sensitive data, the speakers will introduce a wide range of techniques–from simple hashing to advanced embeddings–for high-accuracy, privacy-safe model [[development]].  Key topics include pseudonymous ID generation, semantic scrubbing, structure-preserving data fuzzing, task-specific vs. task-independent sanitization and ensuring downstream privacy in multi-party collaborations. Special attention will be given to Spark-based production environments.  About: [[Databricks]] provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business. Website: https://databricks.com
+However, there is a growing feeling that GDPR has dampened innovation in machine learning & AI applied to personal and/or sensitive data. After all, ML & AI are hungry for rich, detailed data and sanitizing data to improve privacy typically involves redacting or fuzzing inputs, which multiple studies have shown can seriously affect model quality and predictive power. While this is technically true for some privacy-safe modeling techniques, it’s not true in general.  The root cause of the problem is two-fold. First, most data scientists have never learned how to produce great models with great privacy. Second, most companies lack the systems to make privacy-safe machine learning & AI easy. This talk will challenge the implicit assumption that more privacy means worse predictions. Using practical examples from production environments involving personal and sensitive data, the speakers will introduce a wide range of techniques–from simple hashing to advanced [[embedding]]s–for high-accuracy, privacy-safe model [[development]].  Key topics include pseudonymous ID generation, semantic scrubbing, structure-preserving data fuzzing, task-specific vs. task-independent sanitization and ensuring downstream privacy in multi-party collaborations. Special attention will be given to Spark-based production environments.  About: [[Databricks]] provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business. Website: https://databricks.com
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 * [[Policy]]   ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]]
 * [[Blockchain]]
-* [[Bias and Variances]]
+* [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]]
 * [[OpenMined]]
 * [https://www.technologyreview.com/s/612552/facial-recognition-has-to-be-regulated-to-protect-the-public-says-ai-report Facial recognition has to be regulated to protect the public, says AI report | Will Knight - MIT Technology Review]

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 <youtube>ypykT4tqIjc</youtube>
 <b>An Introduction to Private Machine Learning - Singapore [[Python]] User Group
-</b><br>Speaker: Satish Shankar  This talk will introduce the essential concepts from cryptography necessary to build AI systems that use sensitive data and yet protect our privacy. Specifically, we will cover concepts from secure multi-party computation (MPC) and how they can be used to build machine learning algorithms. Why does this matter? This matters because we as a society are struggling to balance the benefits of data driven systems and the privacy risks they create. Building any machine learning or analytics model necessitates the collection of data. If this data is sensitive or personal, it inevitably turns into an honeypot for hackers. At a societal level, we are responding to this issue by introducing more regulation such as the GDPR. Instead of regulations, it is possible to use cryptography to protect our data and still analyze it: This talk show how. About: Shankar leads the machine learning and AI efforts for Manulife’s innovation labs. He works on quantitative investment and insurance, drawing on a wide range of fields from machine learning, natural language processing, differential privacy, encryption, and more. He is particularly interested in the intersection of [[blockchains]], distributed systems and privacy in machine learning.
+</b><br>Speaker: Satish Shankar  This talk will introduce the essential concepts from cryptography necessary to build AI systems that use sensitive data and yet protect our privacy. Specifically, we will cover concepts from secure multi-party computation (MPC) and how they can be used to build machine learning algorithms. Why does this matter? This matters because we as a society are struggling to balance the benefits of data driven systems and the privacy risks they create. Building any [[Machine Learning (ML)]] or analytics model necessitates the collection of data. If this data is sensitive or personal, it inevitably turns into an honeypot for hackers. At a societal level, we are responding to this issue by introducing more regulation such as the GDPR. Instead of regulations, it is possible to use cryptography to protect our data and still analyze it: This talk show how. About: Shankar leads the machine learning and AI efforts for Manulife’s innovation labs. He works on quantitative investment and insurance, drawing on a wide range of fields from machine learning, natural language processing, differential privacy, encryption, and more. He is particularly interested in the intersection of [[blockchains]], distributed systems and privacy in machine learning.
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 * [[Risk, Compliance and Regulation]]  ... [[Ethics]]  ... [[Privacy]]  ... [[Law]]  ... [[AI Governance]]  ... [[AI Verification and Validation]]
 * [[Cybersecurity]] ... [[Open-Source Intelligence - OSINT |OSINT]] ... [[Cybersecurity Frameworks, Architectures & Roadmaps | Frameworks]] ... [[Cybersecurity References|References]] ... [[Offense - Adversarial Threats/Attacks| Offense]] ... [[National Institute of Standards and Technology (NIST)|NIST]] ... [[U.S. Department of Homeland Security (DHS)| DHS]] ... [[Screening; Passenger, Luggage, & Cargo|Screening]] ... [[Law Enforcement]] ... [[Government Services|Government]] ... [[Defense]] ... [[Joint Capabilities Integration and Development System (JCIDS)#Cybersecurity & Acquisition Lifecycle Integration| Lifecycle Integration]] ... [[Cybersecurity Companies/Products|Products]] ... [[Cybersecurity: Evaluating & Selling|Evaluating]]
 * [[Policy]]   ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]]
 * [[Blockchain]]
 * [[Bias and Variances]]
 * [[OpenMined]]
 * [https://www.technologyreview.com/s/612552/facial-recognition-has-to-be-regulated-to-protect-the-public-says-ai-report Facial recognition has to be regulated to protect the public, says AI report | Will Knight - MIT Technology Review]
 * [[Other Challenges]] in Artificial Intelligence
 * [https://www.research.ibm.com/haifa/projects/imt/AI%20Privacy/index.shtml Data Security and Privacy | ][[IBM|IBM Research]]

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-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, TensorFlow, Meta, Facebook, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=privacy+machine+learning+artificial+intelligence YouTube search...]
 [https://www.google.com/search?q=privacy+machine+learning+artificial+intelligence ...Google search]
 * [[Case Studies]]
-* [[Government Services]]
+* [[Cybersecurity]] ... [[Open-Source Intelligence - OSINT |OSINT]] ... [[Cybersecurity Frameworks, Architectures & Roadmaps | Frameworks]] ... [[Cybersecurity References|References]] ... [[Offense - Adversarial Threats/Attacks| Offense]] ... [[National Institute of Standards and Technology (NIST)|NIST]] ... [[U.S. Department of Homeland Security (DHS)| DHS]] ... [[Screening; Passenger, Luggage, & Cargo|Screening]] ... [[Law Enforcement]] ... [[Government Services|Government]] ... [[Defense]] ... [[Joint Capabilities Integration and Development System (JCIDS)#Cybersecurity & Acquisition Lifecycle Integration| Lifecycle Integration]] ... [[Cybersecurity Companies/Products|Products]] ... [[Cybersecurity: Evaluating & Selling|Evaluating]]
 * [[Policy]]   ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]]
-* [[Risk, Compliance and Regulation]]  ... [[Ethics]]  ... [[Privacy]]  ... [[Law]]  ... [[AI Governance]]  ... [[AI Verification and Validation]]
 * [[Blockchain]]
 * [[Bias and Variances]]

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 Differential privacy is a powerful tool for quantifying and solving practical problems related to privacy. Its flexible definition gives it the potential to be applied in a wide range of applications, including Machine Learning applications. [https://towardsdatascience.com/understanding-differential-privacy-85ce191e198a Understanding Differential Privacy - From Intuitions behind a Theory to a Private AI Application | An Nguyen - Towards Data Science]
-Differential privacy is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. Another way to describe differential privacy is as a constraint on the algorithms used to publish aggregate information about a statistical database which limits the disclosure of private information of records whose information is in the database. For example, differentially private algorithms are used by some government agencies to publish demographic information or other statistical aggregates while ensuring confidentiality of survey responses, and by companies to collect information about user behavior while controlling what is visible even to internal analysts. Roughly, an algorithm is differentially private if an observer seeing its output cannot tell if a particular individual's information was used in the computation. Differential privacy is often discussed in the context of identifying individuals whose information may be in a database. Although it does not directly refer to identification and reidentification attacks, differentially private algorithms probably resist such attacks.  Differential privacy was developed by cryptographers and thus is often associated with cryptography, and draws much of its language from cryptography. [https://en.wikipedia.org/wiki/Differential_privacy Wikipedia]
+Differential privacy is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. Another way to describe differential privacy is as a constraint on the algorithms used to publish aggregate information about a statistical database which limits the disclosure of private information of records whose information is in the database. For example, differentially private algorithms are used by some government agencies to publish demographic information or other statistical aggregates while ensuring confidentiality of survey responses, and by companies to collect information about user behavior while controlling what is visible even to internal analysts. Roughly, an algorithm is differentially private if an observer seeing its output cannot tell if a particular individual's information was used in the computation. Differential privacy is often discussed in the [[context]] of identifying individuals whose information may be in a database. Although it does not directly refer to identification and reidentification attacks, differentially private algorithms probably resist such attacks.  Differential privacy was developed by cryptographers and thus is often associated with cryptography, and draws much of its language from cryptography. [https://en.wikipedia.org/wiki/Differential_privacy Wikipedia]
 ==== Local Differential Privacy ====