arXiv subjects

Anders Dalskov

Publications and source records attributed to Anders Dalskov.

3 recordsLinked to original sources

arXiv2026

Information Flow Control in Off-Chain Components

This paper develops a model of a smart-contract language for a blockchain architecture with off-chain components. Off-chain components are pieces of smart contracts that execute at designated locations outside of the network of blockchain nodes, but remain synchronised with the on-chain contract state. They react to changes to the on-chain state, but may also notify the on-chain component about events in the world, e.g. stock prices, weather data etc., or even act as a bridge between different blockchains. This affords greater flexibility for the developer, but may also enable new vulnerabilities. As a concrete example, we use the model to study the problem of ensuring integrity and secrecy of data between the on-chain and off-chain components, using static information flow control techniques. This fails, even in the absence of a loop construct, because off-chain components act as separate threads and can encode a blocking construct e.g. through recursive method calls. We end the paper with a discussion of possible ways to remedy this situation.

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arXiv2021

Secure Evaluation of Quantized Neural Networks

We investigate two questions in this paper: First, we ask to what extent "MPC friendly" models are already supported by major Machine Learning frameworks such as TensorFlow or PyTorch. Prior works provide protocols that only work on fixed-point integers and specialized activation functions, two aspects that are not supported by popular Machine Learning frameworks, and the need for these specialized model representations means that it is hard, and often impossible, to use e.g., TensorFlow to design, train and test models that later have to be evaluated securely. Second, we ask to what extent the functionality for evaluating Neural Networks already exists in general-purpose MPC frameworks. These frameworks have received more scrutiny, are better documented and supported on more platforms. Furthermore, they are typically flexible in terms of the threat model they support. In contrast, most secure evaluation protocols in the literature are targeted to a specific threat model and their implementations are only a "proof-of-concept", making it very hard for their adoption in practice. We answer both of the above questions in a positive way: We observe that the quantization techniques supported by both TensorFlow, PyTorch and MXNet can provide models in a representation that can be evaluated securely; and moreover, that this evaluation can be performed by a general purpose MPC framework. We perform extensive benchmarks to understand the exact trade-offs between different corruption models, network sizes and efficiency. These experiments provide an interesting insight into cost between active and passive security, as well as honest and dishonest majority. Our work shows then that the separating line between existing ML frameworks and existing MPC protocols may be narrower than implicitly suggested by previous works.

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arXiv2020

2FE: Two-Factor Encryption for Cloud Storage

Encrypted cloud storage services are steadily increasing in popularity, with many commercial solutions currently available. In such solutions, the cloud storage is trusted for data availability, but not for confidentiality. Additionally, the user's device is considered secure, and the user is expected to behave correctly. We argue that such assumptions are not met in reality: e.g., users routinely forget passwords and fail to make backups, and users' devices get stolen or become infected with malware. Therefore, we consider a more extensive threat model, where users' devices are susceptible to attacks and common human errors are possible. Given this model, we analyze 10 popular commercial services and show that none of them provides good confidentiality and data availability. Motivated by the lack of adequate solutions in the market, we design a novel scheme called Two-Factor Encryption (2FE) that draws inspiration from two-factor authentication and turns file encryption and decryption into an interactive process where two user devices, like a laptop and a smartphone, must interact. 2FE provides strong confidentiality and availability guarantees, as it withstands compromised cloud storage, one stolen or compromised user device at a time, and various human errors. 2FE achieves this by leveraging secret sharing with additional techniques such as oblivious pseudorandom functions and zero-knowledge proofs. We evaluate 2FE experimentally and show that its performance overhead is small. Finally, we explain how our approach can be adapted to other related use cases such as cryptocurrency wallets.

cs.CR↗