Isaac K. Nti’s Post

Day 4 of ML at iCodeGuru In our 4th session, we dived into the world of Machine Learning and explored its essentials, including: - Target variable and input features - Data loading and analysis through feature extraction - Training models on data - Testing and predicting with ML models Ma'am Zartashia A. then shared a detailed overview of Classification and Regression, highlighting their differences and applications. But what really caught my attention was the introduction to Differential Privacy. So, what is Differential Privacy? It's a technique to protect individuals' privacy when collecting and using data. In today's data-driven world, ensuring data privacy is crucial. #machinelearning #dataprivacy #differentialprivacy #classification #regression #datascience #privacymatters

🌐 Safeguarding Privacy in Machine Learning 🤖 In our latest post, we discussed how differential privacy can protect sensitive information in training data without compromising utility using methods like Differential-private Synthetic Data and DP-SGD. Read more: https://lnkd.in/eRBeKHbq This article is a short primer on one such technique we use for privacy in ML. It is aimed to be part of a series of articles on Privacy and Machine Learning....and how we are using engineering+ research to scale PETs for ML :) #Privacy #DataProvenance #TrainingData #DataSecurity #DifferentialPrivacy #ValyuExchange Harvey Yorke

Dear Slack, I just read your #privacy principles for #AI. I have some questions. I am very glad to learn that you *must be* using #DifferentialPrivacy for model training. Indeed, this is the only mathematical framework that can support the strong claims you make. However, you never mention it anywhere. Why so? Can you confirm that this is indeed the technology at play here? It would be very valuable for the community to be aware of such a broad scale deployment. Also, because obscurity is never the best form of security, any information you may share with the community on how it is implemented would be immensely valuable. Despite the scant details, the strength of your language hints at the fact that you are using the account as the privacy unit and epsilon below, say, 2, am I right? As one of your 65m users, I deeply care about how my data is managed. As cofounder of Sarus (YC W22), I strongly welcome all new adoption of privacy best practices. Two reasons why I really hope I did not misunderstand your policy! Best regards, PS: In the unlikely event where the claims would not be supported by strong math, no worries, Sarus (YC W22) is here to help!

After 10 years of experience in data science, I've witnessed the evolution of the intersection between machine learning (ML) and privacy. As ML projects continue to be a cornerstone of the financial services industry, we must ensure that privacy remains at the forefront of everything we do. To that end, here are some critical privacy considerations to keep in mind when working on ML projects: 🔑 Data Minimization: Collect only what you need. Always question whether each piece of data is necessary for your model. 🔐 Differential Privacy: This technique adds noise to the data or queries to provide privacy guarantees while allowing useful computations. It's a powerful tool for maintaining privacy in ML projects. 🛡️ Federated Learning: Train ML models on local datasets and only share the model updates. This approach can significantly reduce privacy risks. 📚 Education: A well-informed team is your best defense against privacy breaches. Regular training on privacy principles and practices is essential. Remember, privacy is not an afterthought—it's a fundamental aspect of any ML project in the financial industry. #DataScience #MachineLearning #Privacy #FinancialServices

🚨 🤖 Study Reveals Privacy Gaps in GPT Apps 🤖 🚨 A new study from Washington University has found some worrying privacy issues with GPT apps in OpenAI’s GPT Store. The researchers looked at around 120,000 GPTs and 2,500 Actions and found: - Lack of Transparency: Only 5.8% of Actions clearly explain their data practices. - Sensitive Data Exposure: Some apps are collecting passwords and personal info, which is risky. - Data Sharing Concerns: Actions can access each other’s data, which might lead to more privacy issues. This study shows we need better privacy protections and enforcement. Did you know you can opt out of using your chats for model training?? What do you think about these findings?? #Privacy #AI #DataSecurity #OpenAI #DataPrivacy

🔥 It's Data Privacy Week, and we're offering a 20% discount on the 9 remaining seats of our 4-week Privacy, Tech & AI Bootcamp (February). If you are interested, let me know - first come, first served. #privacy #tech #AI #dataprivacyweek

Is Privacy Dead in the Age of AI? 😶 That's definitely a question to ponder. Even though data is collected and used by every business in the world, it is stored and utilized with our consent. We do check the box and read the long pages with small font of terms and services 😅 Currently, I am pursuing my master's in Applied Machine Intelligence (Fancy word for AI). People who aren’t very tech-savvy keep saying so you are the one who has made our lives confusing 😊. Some people give a lot of praise about ChatGPT and Nvidia stocks whereas others call out other LLM models who try to be anti-discriminatory and woke but eventually give historically inaccurate responses. That’s why I am always wondering how I as a future AI/ML engineer can guarantee the safeguard privacy of others' data, explainability of my model to ensure there is no bias and make sure that any data flows throughout the system stays private. Thanks to my professor Mimoza Dimodugno PhD and her class about Data Governance. I came across another essential resource via DataCamp which helps in data anonymization, privacy-preserving techniques via dedicated private machine learning models and how privacy vs utility is always a factor to think about. A big thank you to Rebeca S. González Guerra for this wonderful course. It is a must for anyone who is looking to build a Responsible AI solution. I applied the concepts immediately in one of my challenging hands-on assignments. #ai #privacy #datacamp #anonymity #ml #datagovernance

Enhancing machine learning techniques while safeguarding data privacy is of paramount importance, especially in the sensitive realm of healthcare in regards to patient data.

Federated learning is a machine learning approach that trains algorithms across multiple decentralized devices or servers holding local data samples, without exchanging their data. This method allows for privacy-preserving model training by keeping the data on the originating devices and only sharing model updates (like gradients or model parameters) with a central server. The central server then aggregates these updates to improve the overall model. This way, sensitive data remains local, enhancing privacy and security while still enabling collaborative learning across different data sources. #FederatedLearning #AI #Privacy #DataSecurity #MachineLearning #pyramidcybersecurityandforensic

Differential privacy is necessary in securing sensitive data in AI-driven healthcare and medical projects by introducing a mathematical framework that ensures the privacy of individuals' information. By adding noise or randomness to query responses, it becomes extremely difficult to distinguish specific individuals' data from the aggregated results and this is exactly differential privacy guys. This protection encourages individuals to share their data without fear of their personal information being exposed, promoting a more extensive and diverse dataset for training machine learning algorithms. Although this trade-off can be difficult to manage and requires careful balancing to balance privacy and utility but can be a solution. #privacy #healthcare #medical #artificialintelligence #project