Data breaching and other internet frauds have risen dramatically over the past few years. According to sources, organisations in India lost about ₹12.8 crore on average between July 2018 and April 2019. The global average total cost of data breach was USD 3.92 million (about ₹27.03 crore) with the average size of the breach being 25,575 records.
Data is an important information asset and a crucial part of organisation and big techs are striving hard to protect data from hackers. Cyber-attacks have become one of the top concerns among the big tech companies globally. It is essential for organisations to keep one step ahead of cyber attackers and hackers.
Recently, in our of our articles, we discussed steps taken to protect data by Confidential Computing Consortium created by the Linux Foundation. Tech giants like IBM, Intel, Google, Microsoft, Red Hat among others are members of this data safety consortium. These companies have contributed various toolkits and projects for confidential computing. Data is moving between various computing environments such as on-premises servers, public cloud, etc. and this consortium will assist those data to move freely with high security.
After the trade-ban with the US, China has upped its AIML game and is adopting AI not only in the organisations but also for integrating into educational purposes. In the last couple of months, Chinese researchers, as well as tech giants, have made several contributions in emerging technologies such as introducing hybrid chip known as Tianjic chip to stimulate AGI development, open sourcing Xuantie 910 which is a powerful RISC-V (Reduced Instruction Set Computer) processor.
With fast advancements in the field of emerging technologies, the fear of cyber attacks is also around the corner. For these security issues, Chinese tech giant, Alibaba has been trying to keep safe the data by ensuring data privacy and it has also recently become a member of the Confidential Computing Consortium.
A few days ago, Chinese commercial giant, Alibaba’s subsidiary Ant Financial introduced Shared Machine Learning (SML) as their solution for data privacy. SML is a combination of the Trusted Execution Environment (TEE) and Multiparty Computation (MPC) system. This solution includes Intel’s SGX technology in its foundation layer which is compatible with other TEE implementations and supports both online prediction and offline training. The TEE-based framework supports a variety of commonly used prediction algorithms including LR, GBDT, and Xgboost; and enables prediction on encrypted data from multiple parties while the MPC-based framework supports popular algorithms including LR, GBDT, GNN, etc. The framework is able to perform various tasks mentioned below:
- Solve the problems of load balancing, failover, dynamic expansion and shrinkage, and disaster recovery with the clustering solution
- Provide an easy-to-use development framework for users
- Reduce the user’s access cost through a built-in technology of ServiceProvider with SDK
- Solve problems such as code upgrade, grayscale publishing, and release rollback with multi-cluster management and SDK heartbeat mechanism
- Provide a provision agent mechanism to ensure that SGX does not need to connect to the external network, which improves system security
As consumers move towards a smart society data will be susceptible to breaches. In the digital era, every single important information is residing somewhere on-premise or in the cloud platform. Big tech organisations are trying hard to ensure privacy protection and strong security measures for all personal information.
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