With Obscura, organizations can securely analyze large amounts of unstructured data in near-real time. It consists of three components:

• Obscura Data Sync (ODS): A software provided by Algemetric that is under the control of Algemetric’s customer for accessing customer’s unencrypted database within the customer’s infrastructure. Once data is encrypted / shared, data is doubled encrypted by Spectra using standard cryptography and transmitted to the Obscura Secure Execution Environment.
• Obscura Secure Execution Environment (OSEE): In the trusted executed environment data is kept encrypted with standard symmetric cryptography. Once computation of a particular algorithm is required, the external layer of the double encryption is decrypted, which enables computation on the inner layer of the doubled encrypted data. Plain data is never exposed throughout this process.
• Obscura API (OA): The component responsible for requesting, receiving, and processing results from the OSEE. The OA only sees requests for execution of predefined algorithms and the result of these algorithms. Communication between these three components is always protected by Spectra.

• Customer-centric secure computation: There must be a reconciliation between data availability, security and privacy. Many areas of business and engineering are rapidly expanding the use and the scope of data analytics, business intelligence, machine learning, statistics, among other technologies and techniques while security and privacy violation incidents are each time more frequent and more severe. It is not enough to advance data science and security and privacy in isolation. Without intentional research and development for reconciling utility, security, and privacy, use cases for secure computation will be scarce and limited in purpose and relevance, which will contribute to slower adoption of privacy-enhancing technologies (PETs). We research and develop secure computation in collaboration with our customers so that our technologies and products are a direct reflect of their needs and expectations.
• Security, privacy, and efficiency or nothing at all: When it comes to secure computation, performance requirements must be as indispensable as security and privacy ones. If practical computations of interest cannot be performed on encrypted data via some solution, then such solution should be considered improper, just like insecure and privacy-violating ones would.
• From general-purpose to application specific: Obscura is designed to be a framework for secure computation in which general-purpose algorithms can be computed over encrypted data, as a foundation. From that, application-specific functionalities can be derived and refined for satisfying the requirements of each use case.
• It is about results, not technologies: Obscura is enabled by FHE and MPC but it is not about these technologies. Obscura is about providing desired functionalities over data without incurring in security and privacy issues. We are evangelists of the materialization of these goals over advocating for any technology in particular.

• Encrypted data only: Not all data is sensitive and therefore not everything must be encrypted for allowing computation on encrypted data. Obscura works well with hybrid scenarios in which different data with different purposes might be protected with distinct cryptographic tools. However, Obscura only possesses and processes encrypted data for a significant data breach risk reduction.
• On-database computation: Obscura allows for overriding some native operations in databases for better performance and less development complexity.
• Efficient built-in data encoder: Most of the data handled by Obscura is fractional data (from applications in finance, statistics, machine learning, etc.). Obscura uses a fractional encoder with better efficiency than typical high-precision techniques.

• Secure multiparty computation: The security of Obscura MPC protocol is provided by threshold secret sharing where input data is shared across multiple parties, each one containing meaningless data in isolation. Only upon collecting a required number of distinct shares, the data of interest can be retrieved. Security is achieved by ensuring that no unauthorized entity will control or corrupt such number of distinct shares.
• Fully homomorphic encryption: The security of Obscura FHE is provided by the hardness associated with the Ring Learning With Errors problem (RLWE). Such well-known cryptographic problem is considered to be quantum-resistant.
• Spectra: Obscura uses Spectra for authenticating, authorizing, and auditing communication between each component and for any communication between machines. Therefore, even before the security provided by MPC and FHE takes place, Spectra is protecting data and transactions from the world with standard cryptography.

• Data synchronization: The ODS The is deployed at the customer’s infrastructure under their control and it is responsible for automatic or on-demand synchronization between the unencrypted and the encrypted database on an iterative and incremental basis.
• Algorithm execution: The OSEE receives algorithmic instructions from the OA for execution. These algorithms are pre-approved by the customer under the scope of each particular application.
• Integration with Prisma and other visualization tools: Once results are available, Obscura can be easily integrated with Prisma for a number of data visualization options. Once data is integrated with Prisma, it can also be exported to external visualizations tools.

• Security and privacy compliance: Obscura goes beyond partial measures for security and privacy of data in use by exclusively working with encrypted data at all times.
• Corporate Sovereignty: Obscura helps organizations to protect their control, exclusivity, ownership, and advantages by enabling collaborations in which only aggregated data.
• Anti-data-breach plan: Data breaches can occur for a variety of reasons including product or service malfunction, unaddressed system and architecture vulnerabilities, human error, and malicious behavior. In any case, with Obscura, if a data breach occurs, only encrypted data will be exposed, giving no advantage to unauthorized parties.
• Enablement of economic opportunities and competitive advantage: Obscura is an efficient tool for reconciling utility, security, and privacy, therefore allowing strategic data exploration in a secure and privacy-preserving way. This data exploration enables new revenue streams from subscription-based and on-demand-based services.

• Not yet another hammer looking for a nail: Obscura is built based on insights of organizations in deep need of secure computation. It is therefore a response to concrete inquiries for from those struggling to reconcile utility, security, and privacy.
• Engineered for performance: Every component of Obscura is ruinously engineered for performance. Everything from solution design, libraries, algorithms, optimizations, and infrastructure is carefully researched and developed for satisfying our applications’ performance requirements.
• Crafted by data enthusiasts: At Algemetric, we take data very seriously. Even before considering any security and privacy matter, we are interested in the most efficient ways for data generation, representation, transmission, storage, compression, among other features. We are continuously invested in innovating on how we handle data. By doing so, we are able to find specific combinations of data treatments that significantly favors utility, security, and privacy.