Hiraku Morita

Former Member
Thumbnail Image
Last Name
Morita
First name
Hiraku
Phone
+41 71 224 78 64

Settings

Now showing 1 - 10 of 16
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Constant-Round Client-Aided Two-Server Secure Comparison Protocol and Its Applications
    (2020-01)
    Morita, Hiraku  
    ;
    Attrapadung, Nuttapong
    ;
    Teruya, Tadanori
    ;
    Ohata, Satsuya
    ;
    Nuida, Koji
    Type:journal article
    Journal:IEICE Trans. Fundam. Electron. Commun. Comput. Sci.
    Volume:103-A
    Issue:1
    URL:https://www.jstage.jst.go.jp/article/transfun/E103.A/1/E103.A_2019CIP0023/_article/-char/en
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/112506
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    On the Security of Non-Interactive Key Exchange against Related-Key Attacks
    (2017-09)
    Morita, Hiraku  
    ;
    Schuldt, Jacob C. N.
    ;
    Matsuda, Takahiro
    ;
    Hanaoka, Goichiro
    ;
    Iwata, Tetsu
    Type:journal article
    Journal:IEICE Trans. Fundam. Electron. Commun. Comput. Sci.
    Volume:100-A
    Issue:9
    URL:https://search.ieice.org/bin/summary.php?id=e100-a_9_1910
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/102036
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    On the Security of Schnorr Signatures, DSA, and ElGamal Signatures against Related-Key Attacks
    (2017)
    Morita, Hiraku  
    ;
    Schuldt, Jacob C. N.
    ;
    Matsuda, Takahiro
    ;
    Hanaoka, Goichiro
    ;
    Iwata, Tetsu
    Type:journal article
    Journal:IEICE Trans. Fundam. Electron. Commun. Comput. Sci.
    Volume:100-A
    Issue:1
    URL:https://search.ieice.org/bin/summary.php?id=e100-a_1_73
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/103297
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Mining personal experiences and opinions from Web documents
    (2011)
    Abe, Shuya
    ;
    Inui, Kentaro
    ;
    Hara, Kazuo
    ;
    Morita, Hiraku  
    ;
    Sao, Chitose
    Type:journal article
    Journal:Web Intell. Agent Syst.
    Volume:9
    Issue:2
    URL:https://dl.acm.org/doi/10.5555/1971938.1971940
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/94757
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Adam in Private: Secure and Fast Training of
    Deep Neural Networks with Adaptive Moment
    Estimation
    (2022-06)
    Attrapadung, Nuttapong
    ;
    Hamada, Koki
    ;
    Kikuchi, Ryo
    ;
    Matsuda, Takahiro
    ;
    Mishina, Ibuki
    Abstract: 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.
    Type:conference paper
    DOI:10.56553
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/108656
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Secure Parallel Computation on Privately Partitioned Data and
    Applications
    (2022-11)
    Attrapadung, Nuttapong
    ;
    Morita, Hiraku  
    ;
    Ohara, Kazuma
    ;
    Schuldt, Jacob C. N.
    ;
    Teruya, Tadanori
    Type:conference paper
    DOI:10.114510.1145/3548606.3560695
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/108112
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Memory and Round-Efficient MPC Primitives in the Pre-Processing Model from Unit Vectorization
    (2022-05)
    Attrapadung, Nuttapong
    ;
    Morita, Hiraku  
    ;
    Ohara, Kazuma
    ;
    Schuldt, Jacob C. N.
    ;
    Tozawa, Kazunari
    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.
    Type:conference paper
    DOI:10.1145/3488932.3517407
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/108750
    Scopus© Citations 5
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Oblivious Linear Group Actions and Applications
    (2021-11)
    Attrapadung, Nuttapong
    ;
    Hanaoaka, Goichiro
    ;
    Matsuda, Takahiro
    ;
    Morita, Hiraku  
    ;
    Ohara, Kazuma
    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 roundoptimal (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. An additional feature of our protocols is that they allow to re-invoke a previously generated group action, or its inverse, in subsequent runs. This allows us to utilize randomize-then-reveal techniques, which are crucial for constructing efficient protocols in complex applications. As an application, we construct a new oblivious sorting protocol implementing radix sort. Our protocol is based on a similar approach to the three-party protocol by Chida et al. (IACR ePrint 2019/965), but using our oblivious shuffle as a building block as well as various optimizations, we obtain a two-party protocol (in the client-aided setting) with improved online running time and a reduced number of rounds. As other applications, we also obtain efficient protocols for oblivious selection, oblivious unit-vectorization, oblivious multiplexer, oblivious polynomial evaluation, arithmetic-to-boolean share conversions, and more.
    Type:conference paper
    DOI:1010.1145/3460120.3484584
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/109766
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,