Boening to Use Blockchain in Unmanned Flights

Largest aerospace corporation joined forces with AI company SparkCognition to create a decentralized platform for unmanned aerial vehicles monitoring
18 July 2018   275

Aerospace Corporation Boeing announced its intention to use blockchain technology in monitoring unmanned aerial vehicles, which will be used to carry cargo and implement other scenarios. This is reported by Boeing Mediaroom.

Boeing has entered into an agreement with SparkCognition, an artificial intelligence company, to create a decentralized platform capable of "monitoring unmanned aerial vehicles and assigning traffic corridors and routes," thus ensuring safe transportation of cargo, the company said.

It is expected that the technology will be used in the development of new generation of air vehicles, which the company intends to use to achieve a number of tasks, for example, in the delivery of goods and in urban passenger transportation, similar to the Uber-developed "air taxi service".

Boeing intends to use a "standardized software interface to ensure the supply of goods, production control and other commercial use scenarios." Which blockchain will be used in the project, the company does not specify.

We're at a point in history where technological advances and societal trends are converging to demand bold solutions and a different way to travel. Boeing has the experience and expertise to safely and efficiently shape this emerging world of travel and transport. Through Boeing NeXt, we intend to build on our legacy of opening up new frontiers to move people and goods with proven technologies.
 

Greg Hyslop

Chief technology officer, Boeing

To prepare for the production of such solutions, the world's largest aviation company has established a new division called Boeing NeXt, which will be engaged in research in the field of autonomous flights. Boeing NeXt is also responsible for developing the concept of supersonic passenger flights and other innovative projects.

MIT to Improve Cloud-Based Machine Learning Security

New method combines two encryption techniques and keeps neural networks operate quickly
20 August 2018   124

A team of researchers from MIT presented a combined method of data encryption for cloud artificial intelligence models at a computer security conference organized by USENIX. Protected with its help, the neural network works 20-30 times faster than those that use traditional techniques.

In addition, privacy remains: the cloud server does not receive the full amount of confidential data, and the user remains unaware of the parameters of the neural network. According to researchers, their system could be useful to hospitals for diagnosis of diseases from MRI photographs using cloud-based AI models.

In cloud computing, two techniques are commonly used: homomorphic encryption and garbled circuits. The first receives and performs calculations completely on the encrypted data and generates a result that the user can decode. However, a convolutional neural network creates noise during processing that grows and accumulates with each layer, so the need to filter the interference significantly reduces the computational speed.

The second technique is a form of computation for which two participants are required. The system takes their input data, processes it and sends each its result. In this case, the parties exchange information, but do not have an idea of ​​what it means. However, the width of the communication channel required for data exchange directly depends on the complexity of the calculations.

With respect to cloud neural networks, the technique shows itself well only on nonlinear layers that perform simple operations. On linear, using complex mathematics, the speed is reduced to a critical level.

The MIT team proposed a solution that uses the strengths of these two methods and bypasses the weak ones. So, the user starts on his device an encryption system using the technique of distorted circuits and loads data encrypted with a homomorphic method into the cloud neural network. Thus, both parties to the process are divided by data: the user device performs calculations on the distorted circuits and sends the data back to the neural network.

Separation of the workload allows to bypass the strong noise of data on each layer, which occurs with homomorphic encryption. In addition, the system limits communication on the technique of distorted circuits to only nonlinear layers.

The final touch is protection using the "secret exchange" scheme. When a user downloads encrypted data to a cloud service, they are separated, and each part receives a secret key. During the calculation, each participant has only a portion of the information. They are synchronized at the end, and only then the user requests from the service his secret key to decrypt the results.

As a result, the user gets the result of classification, but remains unaware of the model parameters, and the cloud service does not have access to the entire volume of data, which ensures privacy.

Neural networks require large processing power for processing data, and they are provided by cloud servers. However, MIT researchers are studying another option: the development of chips of a new architecture for the operation of neural networks on the device itself. In February 2018, they introduced a prototype processor, where the calculations are performed 3-7 times faster, and the power consumption is reduced by 95%.