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One of the benefits of e-health records is that they can be encrypted and securely shared with healthcare professionals who are involved in a patient's care, such as doctors, therapists, and trainers. This allows these professionals to access the necessary information to provide care to the patient without having to physically see them in person, which can be especially useful in situations where a patient is unable to come into the office for an in-person visit.
We use PRE technology to ensure patients can control their own personal health data. PRE contains both encryption and authorization functions. By encrypting e-health records, it is possible to reduce the risk of privacy leakage. When a consultation is needed, patients can authorize doctors/hospitals through the PRE network. This means that the information contained in the e-health records can only be accessed by those who have been granted permission to do so, helping to protect the privacy of the patient and their personal health information.
Fully Homomorphic Encryption (FHE) is a useful method for efficiently analyzing large datasets and extracting meaningful insights from encrypted databases.
It can be particularly useful for analyzing trends and patterns in functional data, such as time series data or other data that varies over a continuous domain. We can use techniques such as batching in second-generation FHE to handle large-scale data operations, or functional bootstraping in third-generation FHE to deal with diverse non-linear operations.
It allows for statistical analysis methods for efficient measurement of Key Performance Indicators (KPIs) in a large dataset. The FDFB method involves the use of functional data analysis techniques to estimate the distribution of a functional variable, such as a KPI, across a population.
Privasea enables sharing biometric information securely. Usually an advanced recognition algorithm is used to validate if biometric information is a match or not. However, when the matching algorithm runs the biometric information is usually transferred in a readable format. Using FHE the biometric data remains encrypted so only the matching algorithm can read it and determine whether to grant access or not.
It’s safe even if the server got hacked or your edge device get stolen!
PSI is a cryptographic protocol that allows two parties to compute the intersection of their datasets without revealing any other information (i.e. the information except for the overlaps).
This is useful in cases where the two parties have sensitive data that they want to compare or combine, but they do not want to reveal their database to each other.Private financial information is of a sensitive nature.
Yet, banks and financial institutions occasionally need to cooperate with each other to find common denominators among a customer. For instance, to detect fraudulent or criminal transactions and behaviour. PSI ensures this can be done without compromising personal data. Additionally, banks and financial institutions can cooperate with each other to draw more value out of their data. Advanced analytics is hence powered by the PSI.
Catch up on the latest news and articles.
AI and DePin – they’re two buzzwords of today’s tech landscape. Go on Twitter, Reddit, or any other platform where tech is discussed, and you will see them. Guaranteed.
“Secure LivenessCheck Bot” - Revolutionizing Authentication on Telegram with FHEML by Deploying Privasea AI Network on TON Network.
Privasea AI Network builds on BNB Greenfield, merging privacy-preserving tech with innovative blockchain storage. Experience unparalleled data security, control, and Web3 evolution.
In an age where we're constantly connected, sharing photos, making online purchases, and even attending virtual meetings, the digital footprint we leave behind is vast. But have you ever stopped to think about where all that information goes? This is where the concept of data privacy comes into play.
Fully Homomorphic Encryption is a technology that allows computations on encrypted data without the need for decryption.
Privasea and Mind Network are collaborating to enhance data encryption and secure sharing, paving the way for safer, more efficient data-driven operations focused on privacy.
Ever since the launch of ChatGPT, countless AI tools have been launched. Many bring great solutions, but there’s a downside: data privacy. These AI models need data to be trained on, but with lots of data come lots of problems.Luckily, there’s a solution: Privasea. In this article, we’ll give you an overview of what the Privasea AI Network is, how it works, and what problems it solves. Let’s get into it!
If you would like to know more about the solution we are building or if you want to collaborate in any way, please feel free to reach out on email firstname.lastname@example.org or fill in the form here