What are data silos?
Many of us have experienced the need to bring films, medical records and other information when going to the hospital for medical treatment. Have you ever wondered why? In the medical field, different hospitals and clinics may use different electronic medical record systems and databases. The data formats and interfaces between these systems may be incompatible, resulting in the inability of doctors to directly access and integrate their complete medical record information when patients visit different medical institutions. This is because inconsistent technical standards, strong independence of hospital management, privacy regulations and other restrictions may make medical data difficult to share and integrate. Similarly, many people have experienced that it is very cumbersome to go to different government departments to handle business in the past. This is because different government departments and agencies are responsible for different public services and data collection. For example, the tax department, social security department and health department each manage a large amount of data, but these data are usually not seamlessly integrated and shared, resulting in inefficient public services. Factors such as laws, privacy protection, and independent government structures limit the ability to share and integrate data between government departments. These are the multiple examples of data islands we have heard of. Data islands refer to the phenomenon that data cannot be effectively integrated and shared. There are many reasons for the existence of data islands: 1. Technical barriers: Different systems or platforms use different data formats, storage methods, interface standards, etc., which makes it difficult for data to communicate and use each other. 2. Organizational structure issues: There is a lack of effective data sharing mechanisms and culture between different departments or business units within large organizations, resulting in vertical or functional isolation of data. 3. Legal and privacy issues: Data involves sensitive information or is restricted by laws and regulations, resulting in restrictions or obstacles to data sharing.
4. Data ownership and control: The owner or controller of the data is unwilling or unable to share the data with other entities, which may involve issues such as commercial interests and competitive relationships.
5. Cost and resource limitations: Data integration and sharing may require a lot of resources and costs, and some organizations may not be able or willing to invest these resources.
6. Culture and ideology: Some organizations or individuals may believe that data should be private and are unwilling or uncomfortable sharing data with other parties.
What are the common technical means to solve data silos?
The current research and practice of solving data silos mainly focus on: Federated Learning, Zero-Knowledge Proofs (ZKP) and Fully Homomorphic Encryption (FHE), Secure Multiparty Computation (SMC), Differential Privacy, and Split Learning. Today, due to space constraints, we will not discuss them one by one, but mainly talk about Fully Homomorphic Encryption (FHE). #美国5月核心PCE物价指数年率增幅创2021年3月以来新低 #Mt.Gox将启动偿还计划 #币安合约锦标赛 #VanEck提交首个SolanaETF #VanEck提交首个SolanaETF $BTC
