Enhancing data integrity, confidentiality and authenticity with digital envelopes and federated learning

Recent concerns with data privacy in machine learning have led to the development of privacypreserving machine learning methods, such as Federated Learning [1]. This method involves multiple parties to privately train local machine learning models with their own data, sharing with the global server only the models’ parameters that will be averaged to update the global model. Such environments are constantly at the risk of suffering cyber-attacks that can compromise the information used in the process and/or the complete machine learning training. One of those attacks are known as data poisoning [2], which is a threat to most machine learning models, in particular for the federated learning method, because of the communication design and the different nodes participating in the training. In this work, it was investigated the application of Digital Envelopes [3] combined with Federated Learning, to improve data integrity and authenticity in order to prevent the machine learning models to be training with poisoned data. Also, this combination improves the confidentiality by assuring the information is not made available or disclosed to unauthorized individuals or entities. The proposed approach was able to identify when the dataset was compromised by a corrupted agent, that impacted the results of the machine learning and prevented the specific dataset to participate in the training process.publishe

A HYBRIDIZED ENCRYPTION SCHEME BASED ON ELLIPTIC CURVE CRYPTOGRAPHY FOR SECURING DATA IN SMART HEALTHCARE

Recent developments in smart healthcare have brought us a great deal of convenience. Connecting common objects to the Internet is made possible by the Internet of Things (IoT). These connected gadgets have sensors and actuators for data collection and transfer. However, if users' private health information is compromised or exposed, it will seriously harm their privacy and may endanger their lives. In order to encrypt data and establish perfectly alright access control for such sensitive information, attribute-based encryption (ABE) has typically been used. Traditional ABE, however, has a high processing overhead. As a result, an effective security system algorithm based on ABE and Fully Homomorphic Encryption (FHE) is developed to protect health-related data. ABE is a workable option for one-to-many communication and perfectly alright access management of encrypting data in a cloud environment. Without needing to decode the encrypted data, cloud servers can use the FHE algorithm to take valid actions on it. Because of its potential to provide excellent security with a tiny key size, elliptic curve cryptography (ECC) algorithm is also used. As a result, when compared to related existing methods in the literature, the suggested hybridized algorithm (ABE-FHE-ECC) has reduced computation and storage overheads. A comprehensive safety evidence clearly shows that the suggested method is protected by the Decisional Bilinear Diffie-Hellman postulate. The experimental results demonstrate that this system is more effective for devices with limited resources than the conventional ABE when the system’s performance is assessed by utilizing standard model