In today’s technology-driven landscape in which reducing TCO is top of mind, robust data protection is not merely an option but a necessity. As data, both personal and business-specific, is continuously exchanged, stored, and moved across various platforms and devices, the demand for a secure means of data aggregation and trust enhancement is escalating. Traditional data protection strategies of protecting data at rest and data in motion need to be complemented with protection of data in use. This is where the role of inline memory encryption (IME) becomes critical. It acts as a shield for data in use, thus underpinning confidential computing and ensuring data remains encrypted even when in use. This blog post will guide you through a practical approach to inline memory encryption and confidential computing for enhanced data security.

Understanding inline memory encryption and confidential computing

Inline memory encryption offers a smart answer to data security concerns, encrypting data for storage and decoding only during computation. This is the core concept of confidential computing. As modern applications on personal devices increasingly leverage cloud systems and services, data privacy and security become critical. Confidential computing and zero trust are suggested as solutions, providing data in use protection through hardware-based trusted execution environments. This strategy reduces trust reliance within any compute environment and shrinks hackers’ attack surface.

The security model of confidential computing demands data in use protection, in addition to traditional data at rest and data in motion protection. This usually pertains to data stored in off-chip memory such as DDR memory, regardless of it being volatile or non-volatile memory. Memory encryption suggests data should be encrypted before storage into memory, in either inline or look aside form. The performance demands of modern-day memories for encryption to align with the memory path have given rise to the term inline memory encryption.

There are multiple off-chip memory technologies, and modern NVMS and DDR memory performance requirements make inline encryption the most sensible solution. The XTS algorithm using AES or SM4 ciphers and the GCM algorithm are the commonly used cryptographic algorithms for memory encryption. While the XTS algorithm encrypts data solely for confidentiality, the GCM algorithm provides data encryption and data authentication, but requires extra memory space for metadata storage.

Inline memory encryption is utilized in many systems. When considering inline cipher performance for DDR, the memory performance required for different technologies is considered. For instance, LPDDR5 typically necessitates a data path bandwidth of 25 gigabytes per second. An AES operation involves 10 to 14 rounds of encryption rounds, implying that these 14 rounds must operate at the memory’s required bandwidth. This is achievable with correct pipelining in the crypto engine. Other considerations include minimizing read path latency, support for narrow bursts, memory specific features such as the number of outstanding transactions, data-hazard protection between READ and WRITE paths, and so on. Furthermore, side channel attack protections and data path integrity, a critical factor for robustness in advanced technology nodes, are additional concerns to be taken care without prohibitive PPA overhead.

Ensuring data security in AI and computational storage

The importance of data security is not limited to traditional computing areas but also extends to AI inference and training, which heavily rely on user data. Given the privacy issues and regulatory demands tied to user data, it’s essential to guarantee the encryption of this data, preventing unauthorized access. This necessitates the application of a trusted execution environment and data encryption whenever it’s transported outside this environment. With the advent of new algorithms that call for data sharing and model refinement among multiple parties, it’s crucial to maintain data privacy by implementing appropriate encryption algorithms.

Equally important is the rapidly evolving field of computational storage. The advent of new applications and increasing demands are pushing the boundaries of conventional storage architecture. Solutions such as flexible and composable storage, software-defined storage, and memory duplication and compression algorithms are being introduced to tackle these challenges. Yet, these solutions introduce security vulnerabilities as storage devices operate on raw disk data. To counter this, storage accelerators must be equipped with encryption and decryption capabilities and should manage operations at the storage nodes.

As our computing landscape continues to evolve, we need to address the escalating demand for robust data protection. Inline memory encryption emerges as a key solution, offering data in use protection for confidential computing, securing both personal and business data.

Rambus Inline Memory Encryption IP provide scalable, versatile, and high-performance inline memory encryption solutions that cater to a wide range of application requirements. The ICE-IP-338 is a FIPS certified inline cipher engine supporting AES and SM4, as well as XTS GCM modes of operation. Building on the ICE-IP-338 IP, the ICE-IP-339 provides essential AXI4 operation support, simplifying system integration for XTS operation, delivering confidentiality protection. The IME-IP-340 IP extends basic AXI4 support to narrow data access granularity, as well as AES GCM operations, delivering confidentiality and authentication. Finally, the most recent offering, the Rambus IME-IP-341 guarantees memory encryption with AES-XTS, while supporting the Arm v9 architecture specifications.

For more information, check out my recent IME webinar now available to watch on-demand.

The post A Practical Approach To Inline Memory Encryption And Confidential Computing For Enhanced Data Security appeared first on Semiconductor Engineering.