Hello everyone. Today we are diving into a topic that sets at the intersection of privacy and innovation, privacy, preserving search systems. These systems are revolutionizing how we retrieve information while safeguarding sensitive data. Imagine searching for medical records, legal documents, or financial transactions without compromising confidentiality. This is the promise of modern privacy preserving technologies. Over the next, slides, we'll explore their evolution, core principles, some real world applications, and what the future holds. Let's get started. Okay, so if we look at the evolution of search systems, then early search systems were simple. Think basic keyword matching with no personalization. You typed a query, you got results, and that was it. Fast forward to today, insert systems are extremely sophisticated. They analyze user behavior, they understand context and predict what you need, but with great power came a problem. Privacy as data collection crews, so it concerns regulations like GDPR and CCPA have emerged, pushing engineers to rethink system design. Enter privacy by design. A philosophy where privacy isn't an add-on, but the foundation. Today's systems balance hyper-personalization with robust protections using technologies like data minimization and encryption. This evolution isn't just technical, it's ethical. Now, what makes these systems stick? Three core principles. First, privacy By design. This means building privacy into every layer of the system upfront, and no bandaid fixes. Second data minimization Instead of holding data, systems collect only what's essential for their function. Think of it as a lesser more approach. Third, encryption mechanisms. Data is encrypted at rest. In transit and even during processing together, these principles create a trifecta of protection, but it's not just about technology. Systems also empower users with control over their data. Privacy becomes the default and not an option tying into some technical details. So modern systems go beyond basic encryption. Take private information retrieval. For example, with PIR, you can fetch data from a server without revealing what you searched for. It's like checking a book out of a library without the library knowing which one. How do you do that? there are lattice based cryptography and homomophic encryptions that make this magic happen. Then there is secure multi-party computation where multiple parties collaborate on computations without sharing raw data. imagine something like solving a puzzle together while keeping your pieces hidden. And then if you add query obfuscation to randomized searches and then distributed trust architectures, they will help you to eliminate single points of failure. And then you've got a privacy fortress. These mechanisms work together seamlessly, proving that privacy and functionality aren't mutually exclusive. let's talk about machine learning integration. that's the secret source. So Federated learning trains AI models across decentralized devices, hospitals, for instance, can collaborate on a diagnostic model without sharing patient data. Differential privacy adds mathematical noise to data sets, ensuring individual data points stay anonymous. Homomorphic encryption takes it further. It allows computations on encrypted data like doing math on a locked safe without opening it. It's very interesting and secure enclaves. These are hardware isolated environments like vault inside your computer, protecting sensitive operations. Together these technologies safeguard privacy across the entire machine learning pipeline from training to deployment. There are trade-offs. to be truly honest, it does come at a cost. These enhanced protections impact performance of these systems. For instance, search relevance has seen drop as privacy. Privacy, measures tightened. Imagine, librarian whispering answers, but occasionally getting them wrong. Systems combat this with real time monitoring to optimize accuracy. Query latency also increases, because now you've got multiple systems added to your stack. that's, adding extra heartbeats for getting your results from your search index. And then computational costs, they have skyrocketed because, there is a resource usage. But there is a good news, automated resource allocations here and adaptive tuning keeps these trade offs in check goal being, maximizing privacy without turning your search into a snail. Let's shift to real world impact. And healthcare privacy isn't optional, it's life or death. Systems have, you know where insured patient confidentiality With granular permissions, only authorized doctors can access specific records. Secure retrieval mechanisms comply with HIPAA and GDPR, letting nurses pull critical data during emergencies without exposing sensitive details. Audit trails, log every access like a security camera for data. these systems together prove that even in a high stakes environment, privacy and accessibility can coexist. imagine a searcher, assessing a patient's history instantly, securely and without hesitation. That's the power of privacy, preserving search beyond healthcare. another example is law and finance. Legal discovery platforms handle mountains of sensitive documents. privacy preserving search ensures that confidentiality during these investigations, for example, like fighting a needle in a haystack without revealing the needle. In financial systems, encrypted transactions such as protect your bank records, securely checking your investment portfolio tax history without exposing it to hackers is a very fundamental use case. These sectors show how privacy preserving technologies meet strict regulations while keeping operations efficient. Compliance then becomes built in, and now it's not a hurdle. Now, how successful are these implementations? Let's look at the numbers. these are recorded over multiple use cases that people have built, so I. some examples that we've seen is healthcare systems achieve around 90% success rates. similar, thing for legal platforms a little bit less. They have around, similar adoption, balancing confidentiality with usability. Financial services also see a similar rate, even with real time processing demand. we've seen this in hfds. Each sector faces unique challenges like healthcare's need for instant access or finances, real time requirements, but the results speak for themselves. Privacy, preserving search is proven and practical. Now you're ready to implement. What's the roadmap? So what you can do is you can start off with a modular architecture, embed privacy into every layer, like building blocks, address integration challenges, especially with legacy systems. You cannot just overhaul everything overnight. But incremental upgrades, work deployments require structured monitoring and incident response. Think of it as a fire drill for data breaches. And then you can optimize your performance by balancing security with speed. It's not about technology, it's about strategy. A well planned rollout, ensure success. So future directions and conclusion. What's next? The future is pride. reduced computational overhead will make privacy techniques faster and cheaper. We are seeing it already happen. Scalability improvements handle massive data sets. You can think of global financial networks or genome databases. And then dynamic data handling will secure real time streams like stock trade or, information coming out of the sensors of your iot devices. As regulations tighten, these advancements will redefine how we balance privacy and utility. The next decade isn't just about better search, but it's about safer search. In conclusion, privacy preserving search systems are no longer optional. They are essential from all the use cases that we have discussed. Healthcare to finance These technologies protect sensitive data without sacrificing performance. They proof that privacy isn't a trade off, it's cornerstone of modern innovation. thank you so much for your time. please. I'm happy to answer any questions. I think they might have my email linked so you can reach out to me in case you have anything that you want to talk about. Thank you so much.