Confidential computing – the new HTTPS?

Security by default hasn’t arrived yet.

Over the past few years, it’s become difficult to find a website which is just “http://…”.  This is because the industry has finally realised that security on the web is “a thing”, and also because it has become easy for both servers and clients to set up and use HTTPS connections.  A similar shift may be on its way in computing across cloud, edge, IoT, blockchain, AI/ML and beyond.  We’ve know for a long time that we should encrypt data at rest (in storage) and in transit (on the network), but encrypting it in use (while processing) has been difficult and expensive.  Confidential computing – providing this type of protection for data and algorithms in use, using hardware capabilities such as Trusted Execution Environments (TEEs) – protects data on hosted system or vulnerable environments.

I’ve written several times about TEEs and, of course, the Enarx project of which I’m a co-founder with Nathaniel McCallum (see Enarx for everyone (a quest) and Enarx goes multi-platform for examples).  Enarx uses TEEs, and provides a platform- and language-independent deployment platform to allow you safely to deploy sensitive applications or components (such as micro-services) onto hosts that you don’t trust.  Enarx is, of course, completely open source (we’re using the Apache 2.0 licence, for those with an interest).  Being able to run workloads on hosts that you don’t trust is the promise of confidential computing, which extends normal practice for sensitive data at rest and in transit to data in use:

  • storage: you encrypt your data at rest because you don’t fully trust the underlying storage infrastructure;
  • networking: you encrypt your data in transit because you don’t fully trust the underlying network infrastructure;
  • compute: you encrypt your data in use because you don’t fully trust the underlying compute infrastructure.

I’ve got a lot to say about trust, and the word “fully” in the statements above is important (I actually added it on re-reading what I’d written).  In each case, you have to trust the underlying infrastructure to some degree, whether it’s to deliver your packets or store your blocks, for instance.  In the case of the compute infrastructure, you’re going to have to trust the CPU and associate firmware, just because you can’t really do computing without trusting them (there are techniques such as homomorphic encryption which are beginning to offer some opportunities here, but they’re limited, and the technology still immature).

Questions sometimes come up about whether you should fully trust CPUs, given some of the security problems that have been found with them and also whether they are fully secure against physical attacks on the host in which they reside.

The answer to both questions is “no”, but this is the best technology we currently have available at scale and at a price point to make it generally deployable.  To address the second question, nobody is pretending that this (or any other technology) is fully secure: what we need to do is consider our threat model and decide whether TEEs (in this case) provide sufficient security for our specific requirements.  In terms of the first question, the model that Enarx adopts is to allow decisions to be made at deployment time as to whether you trust a particular set of CPU.  So, for example, of vendor Q’s generation R chips are found to contain a vulnerability, it will be easy to say “refuse to deploy my workloads to R-type CPUs from Q, but continue to deploy to S-type, T-type and U-type chips from Q and any CPUs from vendors P, M and N.”

Author: Mike Bursell

Long-time Open Source and Linux bod, distributed systems security, etc.. Now employed by Red Hat. マイク・バーゼル: オープンソースとLinuxに長く従事。他にも分散セキュリティシステムなども手がける。現在Red Hatのチーフセキュリティアーキテクト

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