The analysis of very sensitive data may require additional security and access controls, as well as prevent full use of cloud computing. However, with the use of secure computing the data can be stored off-site while the keys can be tightly controlled on-site. This allows for a stricter access control via key management, as well as obviates some of the need for additional parallel systems to store and handle said confidential data during analysis.

• Fixed costs reduced by elimination of the secure space
• Operational costs reduced via time saved & productivity increase

This use case concerns manufacturing statistical data, sourced from automation systems or IoT systems within the factory, providing information on performance, production output, logistics, etc. This data might need to be useable within the factory For any system that has to analyze and adjust operations autonomously. The specific drivers of this use case are security of the data in cases where the factory may not be in a completely Secure area, or the staff at the factory must not have access to detailed information – while the automated system needs the ability to calculate said data. One example of problems caused by access by wrong parties is the potential to predict the performance of a company ahead of earnings reports based on factory performance data.

In this use case two different data sets can be merged using two separate keys, and stored in a central location, for example the cloud. Once the data is combined the entities can analyze the combined data without revealing the underlying information. This is extremely useful in cases such as fraud detection, where companies want to be able to analyze customer data for fraudulent activity, specific behavior patterns, or other flags - all without revealing the specific customer entries.

Each DataArmor Gate DB does not allow other parties (even platform providers) to access its data inside.

Each decryption key is only used in its specific DataArmor GateDB and no other party has access to it.

Enabling fraud detections through organizations exchanging information on fraudulent transactions. The data, if exchanged in an unencrypted format, has to be obfuscated and sanitized of all private data – rendering any models built on this basis far less accurate. By using secure computing solutions, entities can exchange their data as encrypted, and run analysis directly over the encrypted data – never accessing the customer information directly. This allows them to find anomalies and create accurate machine learning models that can detect such anomalies over their internal customer data – or request keys to decrypt specific portions of the data set as needed. This makes faster and more accurate detection and prevention of fraud possible.

Secure computing can help in the aggregation of logs from various systems and IoT devices – as any system can encrypt and upload data into the cloud, without the possibility of the cloud service provider having any access to the data itself. While the data cannot be decrypted in the cloud it can be analyzed and transformed – allowing for the use of cloud computing resources and storage for sensitive log data while guaranteeing safety and confidentiality. This is especially important in cases where data needs to be centralized but logs may contain private or sensitive information such as account numbers, credit card numbers, etc. With secure computing both the ease of use and the confidentiality of the data is taken into account.

In cases where sensitive Human Resources information is outsourced to a vendor, data would often need to be obfuscated due to regulations and privacy considerations. However, with secure computing the data can be provided as an encrypted blob – completely unreadable however still allowing the 3rd party to perform analysis and apply specific computations as needed. Both the original and the resultant data is always encrypted and the 3rd party never has access to the underlying information – however their ability to fulfill their obligations is preserved.

Secure computing can help in the training of AI to detect anomalies in medical image data – as generally large data sets are required, while a single medical provider can produce a limited volume of said data. This results in insufficient data being available to create AI models to detect anomalies, as well as some limitations on providing services where patient data would be processed. The problem can be mitigated by the use of secure computing, where patient data would be encrypted and processed only in an encrypted format. This allows for access to a far wider scope of patient records, as well as opens the opportunity to provide the same anomaly detection as a service. Encrypted patient data can be both analyzed for anomalies as well as used for the improvement of the AI model, whereas it can only be decrypted by the originator of the data.

Secure computing increases the security of Peer2Peer assisted computing, where processing is split up between the cloud and various peers. If the data is processed while unencrypted, any peer has access to a component of the data. What is more critical is if an attacker compromises enough peers or gains the ability to redirect work units to specific nodes – if enough work units are collected the attacker can then start reconstructing the original data, filling in the gaps via inference. With the use of secure computing technologies this becomes impossible as all data is processed in an encrypted state and the peers never have access to the keys. Only the workload manager has access to the keys and is able to decrypt the data, while any attackers are left holding only fragments of the encrypted data without any way of decrypting it.

Secure computing can protect the data stored on vehicles which is aggregated from the onboard sensors – to enable such functionality as predicting necessary maintenance intervals or inferring the operational capabilities of the vehicle. This data needs to be protected as oftentimes it contains location data, and with sufficient resolution it can be used to infer the behavior of the owner, approximation of who is traveling in the vehicle, and the general scope of activities. As the data is necessarily stored on the vehicle itself, if it is not encrypted it can be easily accessed during a visit to the mechanic, or even maliciously by anyone that can gain physical access to said vehicle. With the use of secure computing the data can be encrypted, and live on the vehicle only in that format. The vehicle ML models can still operate over the encrypted data but the data cannot be decrypted while it is in use. The data can also be uploaded to a central location in the cloud or on company servers, and processed as an aggregate of a vehicle fleet, decrypted as needed for further analysis or simply kept for compliance reasons.