Driven by application diversification and market needs, software systems are integrating new features rapidly. However, this “feature creep” can compromise software security, as more code carries the risk of more vulnerabilities. This paper presents a system for disabling features activated by common input types, using a component called F-detector to detect feature-associated program control flow branches. The system includes a second component called F-blocker to disable features without disrupting application continuity. It does so by treating unwanted features as unexpected errors and leveraging error virtualization to recover execution, by redirecting it to appropriate existing error handling code. We implemented and evaluated the system on the Linux platform using 145 features from 9 programs, and results show that it can detect and disable all features with few errors, hence, outperforming previous works in terms of vulnerability mitigation through debloating.

Secure aggregation consists of computing the sum of data collected from multiple sources without disclosing these individual inputs. Secure aggregation has been found useful for various applications ranging from electronic voting to smart grid measurements. Recently, federated learning emerged as a new collaborative machine learning technology to train machine learning models. In this work, we study the suitability of secure aggregation based on cryptographic schemes to federated learning. We first provide a formal definition of the problem and suggest a systematic categorization of existing solutions. We further investigate the specific challenges raised by federated learning and analyze the recent dedicated secure aggregation solutions based on cryptographic schemes. We finally share some takeaway messages that would help a secure design of federated learning and identify open research directions in this topic. Based on the takeaway messages, we propose an improved definition of secure aggregation that better fits federated learning.

Federated learning allows multiple parties to collaboratively train a global machine learning (ML) model without sharing their private datasets. To make sure that these local datasets are not leaked, existing works propose to rely on a secure aggregation scheme that allows parties to encrypt their model updates before sending them to the central server that aggregates the encrypted inputs. In this work, we design and evaluate a new secure and fault-tolerant aggregation scheme for federated learning that is robust against client failures. We first develop a threshold-variant of the secure aggregation scheme proposed by Joye and Libert. Using this new building block together with a dedicated decentralized key management scheme and an input encoding solution, we design a privacy-preserving federated learning protocol that, when executed among n clients, can recover from up to failures. Our solution is secure against a malicious aggregator who can manipulate messages to learn clients’ individual inputs. We show that our solution outperforms the state-of-the-art fault-tolerant secure aggregation schemes in terms of computation cost on the client. For example, with an ML model of 100,000 parameters, trained with 600 clients, our protocol is 5.5x faster (1.6x faster in case of 180 clients drop).

Internet of Things (IoT) technology promises to bring new value creation opportunities across all major industrial sectors. This will yield industries to deploy more devices into their networks. A key pillar to ensure the safety and security of the running services on these devices is remote attestation. Unfortunately,existing solutions fail to cope with the recent challenges raised by large IoT networks. In particular, the heterogeneity of the devices used in the network affects the performance of a remote attestation protocol. Another challenge in these networks is their dynamic nature: More IoT devices may be added gradually over time. This poses a problem in terms of key management in remote attestation.