Production systems at Google consist of a constellation of microservices1 that collectively issue O(1010) Remote Procedure Calls (RPCs) per second. When a Google engineer schedules a production workload2, any RPCs issued or received by that workload are protected with ALTS by default. This automatic, zero-configuration protection is provided by Google’s Application Layer Transport Security (ALTS). In addition to the automatic protections conferred on RPC’s, ALTS also facilitates easy service replication, load balancing, and rescheduling across production machines. This paper describes ALTS and explores its deployment over Google’s production infrastructure.
Abstract. Access control is central to security in computer systems. Over the
years, there have been many efforts to explain and to improve access control,
sometimes with logical ideas and tools. This paper is a partial survey and discussion
of the role of logic in access control. It considers logical foundations
for access control and their applications, in particular in languages for security
policies. It focuses on some specific logics and their properties. It is intended
as a written counterpart to a tutorial given at the 2009 International School on
Foundations of Security Analysis and Design.
Access control is central to security in computer systems.
Over the years, there have been many efforts to explain and
to improve access control, sometimes with logical ideas and
tools. This paper is a partial survey and discussion of the
role of logic in access control. It considers logical foundations
for access control and their applications, in particular
in languages for programming security policies.
Today, we’re putting our core web services behind the protections provided by U2F and Google’s account takeover and anomaly detection systems. Not only will this provide phishing resistance through the authentication proxy, but also authorization through IAM roles assigned to the user’s Google account.
- Google account
- U2F Yubikey enrolled and enforced for the users/groups that will be accessing the application.
- An hour or so.
- A global cloud that has been operating at billions of rps for decades. (Beyond the scope of this article.)
(notes from Next ’17)
Types of identities
|Google Account||Service Account||G Suite Domain||Google Group|
|Represents||Employee or User||Application Component||All members of the specified domain||All members of the group|
|Log in to Console?||Yes||No||No||No|
|Notes||An instance can run as a service account.|
I listened to a podcast and cut out the chit-chat, so you don’t have to:
Titan is a tiny security co-processing chip used for encryption, authentication of hardware, authentication of services.
Every piece of hardware in google’s infrastructure can be individually identified and cryptographically verified, and any service using it mutually authenticates to that hardware. This includes servers, networking cards, switches: everything. The Titan chip is one of the ways to accomplish that.
The chip certifies that hardware is in a trusted good state. If this verification fails, the hardware will not boot, and will be replaced.
Every time a new bios is pushed, Titan checks that the code is authentic Google code before allowing it to be installed. It then checks each time that code is booted that it is authentic, before allowing boot to continue.
‘similar in theory to the u2f security keys, everything should have identity, hardware and software. Everything’s identity is checked all the time.’
Suggestions that it plays important role in hardware level data encryption, key management systems, etc.
Each chip is fused with a unique identifier. Done sequentially, so can verify it’s part of inventory sequence.
Three main functions: RNG, crypto engine, and monotonic counter. First two are self-explanatory. Monotonic counter to protect against replay attacks, and make logs tamper evident.
Sits between ROM and RAM, to provide signature valididation of the first 8KB of BIOS on installation and boot up.
Produced entirely within google. Design and process to ensure provenance. Have used other vendor’s security coprocessors in the past, but want to ensure they understand/know the whole truth.
Google folks unaware of any other cloud that uses TPMs, etc to verify every piece of hardware and software running on it.
Several papers have already shown the interest of using multiple classifiers in order to enhance the performance of biometric person authentication systems. In this paper, we would like to argue that the core task of Biometric Person Authentication is actually a multiple classifier problem as such: indeed, in order to reach state-of-the-art performance, we argue that all current systems , in one way or another, try to solve several tasks simultaneously and that without such joint training (or sharing), they would not succeed as well. We explain hereafter this perspective, and according to it, we propose some ways to take advantage of it, ranging from more parameter sharing to similarity learning.