Non-interactive and Output Expressive Private Comparison from Homomorphic Encryption

Lu, Wenjie; Zhou, Junjie; Sakuma, Jun

doi:10.1145/3196494.3196503

Cited by 23 publications

(14 citation statements)

References 13 publications

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“…De Cock et al [20] follow the same idea as some previous schemes by first running comparisons. In contrast to all other protocols (ours included), which are secure in the computational setting, they operate in the information theoretic model using secret sharing based SMC and commodity-based cryptography [4] to reduce the [41] propose a non-interactive comparison protocol called XCMP using BGV homomorphic scheme [8]. They then implement the private decision tree protocol of Tai et al [48] using XCMP which is output expressive (i.e., it preserves additive homomorphism).…”

Section: Related Workmentioning

confidence: 99%

Non-Interactive Private Decision Tree Evaluation

Tueno¹,

Boev²,

Kerschbaum³

2019

Preprint

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Decision trees are a powerful prediction model with many applications in statistics, data mining, and machine learning. In some settings, the model and the data to be classified may contain sensitive information belonging to different parties. In this paper, we, therefore, address the problem of privately evaluating a decision tree on private data. This scenario consists of a server holding a private decision tree model and a client interested in classifying its private attribute vector using the servers private model. The goal of the computation is to obtain the classification while preserving the privacy of both the decision tree and the client input. After the computation, the classification result is revealed only to the client, and nothing else is revealed neither to the client nor to the server. Existing privacy-preserving protocols that address this problem use or combine different generic secure multiparty computation approaches resulting in several interactions between the client and the server. Our goal is to design and implement a novel client-server protocol that delegates the complete tree evaluation to the server while preserving privacy and reducing the overhead. The idea is to use fully (somewhat) homomorphic encryption and evaluate the tree on ciphertexts encrypted under the client's public key. However, since current somewhat homomorphic encryption schemes have high overhead, we combine efficient data representations with different algorithmic optimizations to keep the computational overhead and the communication cost low. As a result, we are able to provide the first non-interactive protocol, that allows the client to delegate the evaluation to the server by sending an encrypted input and receiving only the encryption of the result. Our scheme has only one round and can evaluate a complete tree of depth 10 within seconds.

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Section: Related Workmentioning

confidence: 99%

Non-Interactive Private Decision Tree Evaluation

Tueno¹,

Boev²,

Kerschbaum³

2019

Preprint

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“…Cryptography Research Group at Microsoft Research developed Simple Encrypted Arithmetic Library (SEAL) [1], providing a simple and practical software infrastructure using the FV homomorphic encryption scheme for homomorphic applications [5]. SEAL already gained recognition in the literature [11], [12], [13]. In our proof of concept framework, we utilize our NTT-based polynomial multiplier design to accelerate SEAL software by offloading large degree polynomial multiplication operations to the hardware accelerator implemented on FPGA board.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Fast and Scalable NTT-Based Polynomial Multiplier Architecture

Mert

Öztürk

Savaş

2019

2019 22nd Euromicro Conference on Digital System Design (DSD)

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In this paper, we present an optimized FPGA implementation of a novel, fast and highly parallelized NTTbased polynomial multiplier architecture, which proves to be effective as an accelerator for lattice-based homomorphic cryptographic schemes. As I/O operations are as time-consuming as NTT operations during homomorphic computations in a host processor/accelerator setting, instead of achieving the fastest NTT implementation possible on the target FPGA, we focus on a balanced time performance between the NTT and I/O operations. Even with this goal, we achieved the fastest NTT implementation in literature, to the best of our knowledge. For proof of concept, we utilize our architecture in a framework for Fan-Vercauteren (FV) homomorphic encryption scheme, utilizing a hardware/software co-design approach, in which polynomial multiplication operations are offloaded to the accelerator via PCIe bus while the rest of operations in the FV scheme are executed in software running on an off-the-shelf desktop computer. Specifically, our framework is optimized to accelerate Simple Encrypted Arithmetic Library (SEAL), developed by the Cryptography Research Group at Microsoft Research [1], for the FV encryption scheme, where large degree polynomial multiplications are utilized extensively. The hardware part of the proposed framework targets Xilinx Virtex-7 FPGA device and the proposed framework achieves almost 11x latency speedup for the offloaded operations compared to their pure software implementations. We achieved a throughput of almost 800K polynomial multiplications per second, for polynomials of degree 1024 with 32-bit coefficients.

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“…The GC contains 3t AND-gates for comparison and multiplexing (for selecting the next node to traverse after comparison) 4. Tueno et al[42] suggested handling sparse trees by OAI over fewer (than 2 ) nodes; however, this leaks the tree structure, so this paper treats it as 2 .5Table Iomits "non-interactive" (i.e., round-optimal) protocols[33],[41], which require public-key somewhat HE and heavier computation. For example, Lu et al[33] transform the 4-round protocol of Tai et al[40] into 2-round 6.…”

mentioning

confidence: 99%

“…Tueno et al[42] suggested handling sparse trees by OAI over fewer (than 2 ) nodes; however, this leaks the tree structure, so this paper treats it as 2 .5Table Iomits "non-interactive" (i.e., round-optimal) protocols[33],[41], which require public-key somewhat HE and heavier computation. For example, Lu et al[33] transform the 4-round protocol of Tai et al[40] into 2-round 6. This computation model was also used for securely computing/outsourcing other tasks (e.g., private set intersection[13] and linear mean classifiers[44]).…”

mentioning

confidence: 99%

Let’s Stride Blindfolded in a Forest: Sublinear Multi-Client Decision Trees Evaluation

Jack

Tai

Zhao

et al. 2021

Proceedings 2021 Network and Distributed System Security Symposium

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Decision trees are popular machine-learning classification models due to their simplicity and effectiveness. Tai et al. (ESORICS '17) propose a privacy-preserving decision-tree evaluation protocol purely based on additive homomorphic encryption, without introducing dummy nodes for hiding the tree structure, but it runs a secure comparison for each decision node, resulting in linear complexity. Later protocols (DBSEC '18, PETS '19) achieve sublinear (client-side) complexity, yet the server-side path evaluation requires oblivious transfer among 2 d real and dummy nodes even for a sparse tree of depth d to hide the tree structure. This paper aims for the best of both worlds and hence the most lightweight protocol to date. Our complete-tree protocol can be easily extended to the sparse-tree setting and the reusable outsourcing setting: a model owner (resp. client) can outsource the decision tree (resp. attributes) to two non-colluding servers for classifications. The outsourced extension supports multi-client joint evaluation, which is the first of its kind without using multikey fully-homomorphic encryption (TDSC '19). We also extend our protocol for achieving privacy against malicious adversaries. Our experiments compare in various network settings our offline and online communication costs and the online computation time with the prior sublinear protocol of Tueno et al. (PETS '19) and O(1)-round linear protocols of Kiss et al. (PETS '19), which can be seen as garbled circuit variants of Tai et al.'s. Our protocols are shown to be desirable for IoT-like scenarios with weak clients and big-data scenarios with high-dimensional feature vectors.

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Non-interactive and Output Expressive Private Comparison from Homomorphic Encryption

Cited by 23 publications

References 13 publications

Non-Interactive Private Decision Tree Evaluation

Non-Interactive Private Decision Tree Evaluation

Design and Implementation of a Fast and Scalable NTT-Based Polynomial Multiplier Architecture

Let’s Stride Blindfolded in a Forest: Sublinear Multi-Client Decision Trees Evaluation

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