Diggi: A Secure Framework for Hosting Native Cloud Functions with Minimal Trust

Gjerdrum, Anders Tungeland; Johansen, Håvard D.; Brenna, Lars; Johansen, Dag

doi:10.1109/tps-isa48467.2019.00012

Cited by 3 publications

(7 citation statements)

References 14 publications

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“…In the scope of this section, most of the surveyed works (i.e. [66][67][68][69][70]) focus on the exploitation of specialised hardware resources, the Intel SGX, that permit creating enclaves 8 to protect the memory and the execution of functions from superusers of certain machines. Intel SGX permits creating a Trust Computing Base (TCB) in order to protect the execution from possibly malicious FaaS providers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Gjerdrum et al [69] present Diggi, a FaaS framework to execute secure distributed functions while maintaining a small TCB. The framework runtime is split into two parts, one considered trusted and one considered untrusted.…”

Section: Literature Reviewmentioning

confidence: 99%

“…-the Protection technique employed, individuated in -Function sandbox, that is used to isolate the functions by means of specialised hardware or runtime support, -Remote attestation, which consists in acknowledging securely clients or other functions, Diggi [69] Se-Lambda [70] Trapeze [71] Valve [72] Boucher et al [74] Sledger [75] Gerasimov [41] -Key management systems, that protect and distribute encryption keys, -Information flow control, employed to recognise information leaks during the communication flow, -Defence in depth, which is the strategy to stratify a system defence around the assets, -Function type system, intend as typing input and output of functions to avoid type errors in a chain.…”

Section: Summary At a Glancementioning

confidence: 99%

“…It is worth mentioning that the main focus is on the protection of confidentiality of data provided to serverless functions and on the results of their computation, indeed, all the surveyed solutions but [41,74] are defending such asset. Data integrity is less considered, only half of the works surveyed take it in account [66][67][68][69]. The integrity of functions is also an important aspect of the serverless scenario that has been targeted by [41,66,[68][69][70]74,75].…”

Section: Summary At a Glancementioning

confidence: 99%

“…Data integrity is less considered, only half of the works surveyed take it in account [66][67][68][69]. The integrity of functions is also an important aspect of the serverless scenario that has been targeted by [41,66,[68][69][70]74,75]. Correct billing is an asset considered only by [67], even if costs are always pinpointed a key characteristic of the FaaS model.…”

Section: Summary At a Glancementioning

confidence: 99%

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Secure FaaS orchestration in the fog: how far are we?

et al. 2021

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Function-as-a-Service (FaaS) allows developers to define, orchestrate and run modular event-based pieces of code on virtualised resources, without the burden of managing the underlying infrastructure nor the life-cycle of such pieces of code. Indeed, FaaS providers offer resource auto-provisioning, auto-scaling and pay-per-use billing at no costs for idle time. This makes it easy to scale running code and it represents an effective and increasingly adopted way to deliver software. This article aims at offering an overview of the existing literature in the field of next-gen FaaS from three different perspectives: (i) the definition of FaaS orchestrations, (ii) the execution of FaaS orchestrations in Fog computing environments, and (iii) the security of FaaS orchestrations. Our analysis identify trends and gaps in the literature, paving the way to further research on securing FaaS orchestrations in Fog computing landscapes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Summary At a Glancementioning

confidence: 99%

Section: Summary At a Glancementioning

confidence: 99%

Section: Summary At a Glancementioning

confidence: 99%

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Secure FaaS orchestration in the fog: how far are we?

et al. 2021

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Are Machine Learning Cloud APIs Used Correctly?

Wan

Liu

Hoffmann

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

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University o f Chicago {cw an, s h ic h e n g 2 0 0 0 , h a n k h o ffm a n n , m m a ire , s h a n lu } @ u c h ic a g o .e d u Abstract-Machine learning (ML) cloudAPIs enable developers to easily incorporate learning solutions into software systems. Unfortunately, ML APIs are challenging to use correctly and efficiently, given their unique semantics, data requirements, and accuracy-performance tradeoffs. Much prior work has studied how to develop ML APIs or ML cloud services, but not how open-source applications are using ML APIs. In this paper, we manually studied 360 representative open-source applications that use Google or AWS cloud-based m L APIs, and found 70% of these applications contain API misuses in their latest versions that degrade functional, performance, or economical quality of the software. We have generalized 8 anti-patterns based on our manual study and developed automated checkers that identify hundreds of more applications that contain ML API misuses. I. In t r o d u c t io n A. MotivationMachine learning (ML) provides efficient solutions for a number of problems that were difficult to solve with traditional computing techniques; e.g., object detection and language translation. ML cloud APIs allow programmers to incorporate these learning solutions into software systems without designing and training the learning model themselves [1], and hence put these powerful techniques into the hands of non-experts. Indeed, there are more than 35,000 open-source projects on GitHub that use Google or Amazon ML Cloud APIs to solve a wide variety of problems, among which more than 14,000 were created within the last 1 2 months.While these APIs make it easy for non-experts to incorporate learning into software systems, there are still a number of challenges that must be addressed to ensure that the resulting applications are both correct and efficient. While certain challenges come with the use of any third-party API, this paper focuses on unique challenges for ML APIs that arise due to the nature of learning itself. Complicated data requirements. Machine learning tech-niques are used to process digitalized real-world visual, audio and text content. Although such content can be generated by a huge variety of devices and encoding software, the suitable input content and format (encoding, resolution, size, etc.) for ML APIs are rather limited and often uniquely defined by the DNN-training process. For example, cameras can produce images in many formats, but the image sets on which ML models are trained have a relatively small variety [2 ]- [8 ]. Thus, it is up to the API user to select the input or convert the input into what the API can accept and effectively process.

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