Abstract Card-based cryptography allows us to securely compute arbitrary functions using a deck of physical cards. Its performance is mainly measured by the number of used cards and shuffles, and there is a line of work that aims to reduce either of them. One seminal work is the card-based garbled circuit technique by Shinagawa and Nuida (Discret Appl Math 289:248–261, 2021, https://doi.org/10.1016/j.dam.2020.10.013 ), which allows the construction of a card-based protocol for any Boolean function with a single shuffle. Their construction requires $$2n + 24g$$ 2 n + 24 g cards for an n-input Boolean function that is represented by g logical gates. In this paper, we reduce the number of cards to $$2n + 8g$$ 2 n + 8 g for arbitrary functions while keeping it working with only one shuffle. In addition, we propose two types of extensions to support numerical encoding and multi-input gates. In the extended scheme, the free-ADD technique, obtained by generalizing the free-XOR technique by Manabe and Shinagawa (Deng J, Kolesnikov V, Schwarzmann AA (eds) CANS 2023, LNCS, vol 14342. Springer, Singapore, pp 232–248, 2023, https://doi.org/10.1007/978-981-99-7563-1-11 ), is available. The free-ADD technique allows our scheme to evaluate any n-input symmetric Boolean function using $$2n^2+6n+2$$ 2 n 2 + 6 n + 2 cards.

Decision 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.

In this paper, we propose memory- and round-efficient protocols for securely evaluating arithmetic primitives. We focus on secure two-party computation over the ring ℤ2k that achieves security against semi-honest adversaries and works in the pre-processing model. Our protocols rely on the unit vectorization technique introduced by Boyle et al. (TCC 2019). The unit vectorization technique provides online-optimal protocols for several fundamental operations in the pre-processing model. However, a relatively large memory cost for correlated randomness is required, which might become an obstacle in a large-scale application. In order to achieve both memory and communication efficiency, we propose a size reduction method that uses unit vectorization only for short-length inputs, and based on this, construct two-round protocols for equality test, detecting the most significant non-zero bit, detecting wrap-around, and less-than comparison. In addition, as applications of these results, we provide practically efficient protocols for integer division, integer square root, integer logarithm, and modular exponentiation.

Parallel computation is an important aspect of multi-party computation, not only in terms of improving efficiency, but also in terms of providing privacy for computation involving conditional branching based on private data. While applying multi-party computation in parallel over several sets of input data is straightforward if the partitioning of the input data into sets is publicly known, the problem becomes much more challenging when this partitioning is private. This setting is relevant to broad class of secure computations, in particular to secure graph and database analysis in which the underlying data (graph or database) is private. In this paper, we consider a general class of functions which can be expressed via the iterative evaluation of a binary associative operation, and propose efficient protocols for evaluating such functions in parallel over privately partitioned input data. Our protocols are optimal in terms of the required number of evaluations of the underlying binary operation (i.e.\ N-1 evaluations for total input size N), while simultaneously achieving a round complexity which is only logarithmic in the total size of the input data (i.e.\ O(łog N)).

Machine Learning (ML) algorithms, especially deep neural networks (DNN), have proven themselves to be extremely useful tools for data analysis, and are increasingly being deployed in systems operating on sensitive data, such as recommendation systems, banking fraud detection, and healthcare systems. This underscores the need for privacy-preserving ML (PPML) systems, and has inspired a line of research into how such systems can be constructed efficiently. However, most prior works on PPML achieve efficiency by requiring advanced ML algorithms to be simplified or substituted with approximated variants that are “MPC-friendly” before multi-party computation (MPC) techniques are applied to obtain a PPML systems. A drawback of this approach is that it requires careful fine-tuning of the combined ML and MPC algorithms, and might lead to less efficient algorithms or inferior quality ML (such as lower prediction accuracy). This is an issue for secure training of DNNs in particular, as this involves several arithmetic algorithms that are thought to be “MPCunfriendly”, namely, integer division, exponentiation, inversion, and square root extraction. In this work, we take a structurally different approach and propose a framework that allows efficient and secure evaluation of full-fledged state-of-the-art ML algorithms via secure multi-party computation. Specifically, we propose secure and efficient protocols for the above seemingly MPC-unfriendly computations (but which are essential to DNN). Our protocols are three-party protocols in the honest-majority setting, and we propose both passively secure and actively secure with abort variants. A notable feature of our protocols is that they simultaneously provide high accuracy and efficiency. This framework enables us to efficiently and securely compute modern ML algorithms such as Adam (Adaptive moment estimation) and the softmax function “as is”, without resorting to approximations. As a result, we obtain secure DNN training that outperforms state-of-the-art threeparty systems; our full training is up to 6.7 times faster than just the online phase of FALCON (Wagh et al. at PETS’21) and up to 4.2 times faster than Dalskov et al. (USENIX’21) on the standard benchmark network for secure training of DNNs. The potential advantage of our approach is even greater when considering more complex realistic networks. To demonstrate this, we perform measurements on real-world DNNs, AlexNet and VGG16, which are large networks containing millions of parameters. The performance of our framework for these networks is up to a factor of 26 ∼ 33 faster for AlexNet and 48 ∼ 51 faster for VGG16 to achieve an accuracy of 60% and 70%, respectively, when compared to FALCON. Even compared to CRYPTGPU (Tan et al. IEEE S&P’21), which is optimized for and runs on powerful GPUs, our framework achieves a factor of 2.1 and 4.1 faster performance, respectively, on these networks.

In this paper we propose efficient two-party protocols for obliviously applying a (possibly random) linear group action to a data set. Our protocols capture various applications such as oblivious shuffles, circular shifts, matrix multiplications, to name just a few. A notable feature enjoyed by our protocols, is that they admit a round-optimal (more precisely, one-round) online computation phase, once an input-independent off-line computation phase has been completed. Our oblivious shuffle is the first to achieve a round-optimal online phase. The most efficient instantiations of our protocols are obtained in the so-called client-aided client-server setting, where the offline phase is run by a semi-honest input party (client) who will then distribute the generated correlated randomness to the computing parties (servers). When comparing the total running time to the previous best two-party oblivious shuffle protocol by Chase et al. (Asiacrypt 2020), our shuffle protocol in this client-aided setting is up to 105 times and 152 times faster, in the LAN and WAN setting, respectively. We additionally show how the Chase et al. protocol (which is a standard two-party protocol) can be modified to leverage the advantages of the client-aided setting, but show that, even doing so, our scheme is still two times faster in the online phase and 1.34 times faster in total on average.

A secure integer comparison protocol is one of the most fundamental building blocks to construct protocols of rich functionality in multi-party computation. It allows parties to compute the less-than functionality on shared values in privacy preserving manner. In this paper, we present a tree-based secure two-party comparison protocol in the client-aided client-server model, which outperforms existing approaches in terms of round complexity when it is used for 64-bit data. Our proposed protocol requires only 9 communication rounds to compare 64-bit data, which is at least 3 times fewer rounds than existing protocols. This suggests that our protocol is adequate to be used in low-latency networks such as WAN.

We present a new secure integer division protocol with private divisor. Our protocol is based loosely on the Bogdanov et al. (Int. J. Inf. Secur.'12) protocol, which securely computes the classical Goldschmidt's division algorithm. While the Bogdanov et al. scheme was designed specifically to work only on a 3-out-of-3 secret sharing scheme, our scheme works on a 2-out-of-2 secret sharing scheme. This has an advantage since the latter setting is more widely used in the literature of secure computation, and our protocol can thus be used as an efficient building block in this setting. We implement our protocol in Python and provide its benchmark. As a main application of our division protocol, we implement a secure protocol for privacy-preserving chi-squared tests on genomic data. This demonstrates that the proposed protocol is suitable for the statistical analysis on sensitive data.

Secure multi-party computation (MPC) allows a set of parties to jointly compute a function, while keeping their inputs private. We can consider many applications of MPC and various operations/protocols for MPC have been proposed. We focus on (privacy-preserving) division protocols in this paper. Although division is included in four arithmetic operations, we need extremely high computation/communication costs in privacy-preserving settings compared with other operations. Especially, we need many communication rounds for error correction to get accurate quotients. Since we iterate error correction procedure for several times in the division protocol, we can expect to reduce communication rounds by removing error correcting iterations. In this strategy, however, we cannot obtain accurate quotients. In this paper, we show experimental results about a relation between accuracy of quotients obtained by the round-reduced division protocol and the number of communication rounds we need. From our results, we find the error of quotients becomes less than 0.1% even if we reduce the number of communication rounds for error correction to 33%. This property will be useful when we make concrete applications efficient in some cases.

This paper proposes a new UGC-oriented language technology application, which we call experience mining. Experience mining aims at automatically collecting instances of personal experiences as well as opinions from vast amounts of user generated cont

This paper proposes a new UGC-oriented language technology application, which we call experience mining. Experience mining aims at automatically collecting instances of personal experiences as well as opinions from an explosive number of user generated contents (UGCs) such as Weblog and forum posts and storing them in an experience database with semantically rich indices. After arguing the technical issues of this new task, we focus on the central problem, factuality analysis, among others and propose a machine learning-based solution as well as the task definition itself. Our empirical evaluation indicates that our factuality analysis task is sufficiently well-defined to achieve a high inter-annotator agreement and our factorial CRF-based model considerably outperforms the baseline. We also present an application system, which currently stores over 50M experience instances extracted from 150M Japanese blog posts with semantic indices and is scheduled to start serving as an experience search engine for unrestricted users in October.