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PublicationConstant-Round Private Decision Tree Evaluation for Secret Shared DataDecision tree evaluation is extensively used in machine learning to construct accurate classification models. Often in the cloud-assisted communication paradigm cloud servers execute remote evaluations of classification models using clients' data. In this setting, the need for private decision tree evaluation (PDTE) has emerged to guarantee no leakage of information for the client's input nor the service provider's trained model i.e., decision tree. In this paper, we propose a private decision tree evaluation protocol based on the three-party replicated secret sharing (RSS) scheme. This enables us to securely classify inputs without any leakage of the provided input or the trained decision tree model. Our protocol only requires constant rounds of communication among servers, which is useful in a network with longer delays.Ma et al. (NDSS 2021) presented a lightweight PDTE protocol with sublinear communication cost with linear round complexity in the size of the input data. This protocol works well in the low latency network such as LAN while its total execution time is unfavourably increased in the WAN setting. In contrast, Tsuchida et al. (ProvSec 2020) constructed a constant round PDTE protocol at the cost of communication complexity, which works well in the WAN setting. Although their construction still requires 25 rounds, it showed a possible direction on how to make constant round PDTE protocols. Ji et al. (IEEE Transactions on Dependable and Secure Computing) presented a simplified PDTE with constant rounds using the function secret sharing (FSS) at the cost of communication complexity. Our proposed protocol only requires five rounds among the employed three servers executing secret sharing schemes, which is comparable to previously proposed protocols that are based on garbled circuits and homomorphic encryption. To further demonstrate the efficiency of our protocol, we evaluated it using real-world classification datasets. The evaluation results indicate that our protocol provides better concrete performance in the WAN setting that has a large network delay.
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PublicationEfficient Two-Party Secure Aggregation via Incremental Distributed Point Function( 2024)
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PublicationNomadic: Normalising Maliciously-Secure Distance with Cosine Similarity for Two-Party Biometric Authentication(ACM Asia Conference on Computer and Communications Security (ASIA CCS ’24), 2024)
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PublicationEfficient Three-party Boolean-to-Arithmetic Share ConversionThe advantage of mixed-protocol multi-party secure computation frameworks lies in their ability to utilize different sharing types optimally for diverse tasks. A key module in these frameworks are Boolean-to-arithmetic secret sharing conversion protocols, which transfer a secret value from Boolean secret sharing to arithmetic secret sharing. This conversion process can either take in the Boolean secret sharing of a bit or a secret binary string. In this work, we suggest the application of an innovative correlated random tuple for this task in the semi-honest three-party (3PC) setting. This tuple provides the basis for building Boolean-to-arithmetic share conversion protocols. Specifically, we propose two such protocols in the semi-honest 3PC setting, the first protocol takes as input the Boolean secret sharing of a bit, and the second protocol takes as input the Boolean secret sharing of a secret binary string. When it comes to concrete efficiency, the first protocol shows superior performance compared to the existing state-of-the-art in ABY3 (CCS '18). It achieves this by reducing the total required communication from $2\ell$ bits per party to $4\ell/3+1$ bits (including $4\ell/3$ bits in the setup phase, and $1$ bit in the online phase) per party, while maintaining a single round of optimized communication. On the other hand, the second protocol involves two rounds of online communication and its communication cost is comparable to that of ABY2.0 (USENIX'21) that relies on correlated oblivious transfer.
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PublicationPrivate Robust & Efficient Computation of Aggregate Statistics( 2023-06-19)