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Ding, Hangchao, Han Jiang, and Qiuliang Xu. "Postquantum Cut-and-Choose Oblivious Transfer Protocol Based on LWE." Security and Communication Networks 2021 (September 8, 2021): 1–15. http://dx.doi.org/10.1155/2021/9974604.
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We propose postquantum universal composable (UC) cut-and-choose oblivious transfer (CCOT) protocol under the malicious adversary model. In secure two-party computation, we construct s copies’ garbled circuits, including half check circuit and half evaluation circuit. The sender can transfer the key to the receiver by CCOT protocol. Compared to PVW-OT [6] framework, we invoke WQ-OT [35] framework with reusability of common random string ( crs ) and better security. Relying on LWE’s assumption and the property of the Rounding function, we construct an UC-CCOT protocol, which can resist quantum attack in secure two-party computation.
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Fang, Xin, Stratis Ioannidis, and Miriam Leeser. "SIFO: Secure Computational Infrastructure Using FPGA Overlays." International Journal of Reconfigurable Computing 2019 (December 6, 2019): 1–18. http://dx.doi.org/10.1155/2019/1439763.
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Secure Function Evaluation (SFE) has received recent attention due to the massive collection and mining of personal data, but remains impractical due to its large computational cost. Garbled Circuits (GC) is a protocol for implementing SFE which can evaluate any function that can be expressed as a Boolean circuit and obtain the result while keeping each party’s input private. Recent advances have led to a surge of garbled circuit implementations in software for a variety of different tasks. However, these implementations are inefficient, and therefore GC is not widely used, especially for large problems. This research investigates, implements, and evaluates secure computation generation using a heterogeneous computing platform featuring FPGAs. We have designed and implemented SIFO: secure computational infrastructure using FPGA overlays. Unlike traditional FPGA design, a coarse-grained overlay architecture is adopted which supports mapping SFE problems that are too large to map to a single FPGA. Host tools provided include SFE problem generator, parser, and automatic host code generation. Our design allows repurposing an FPGA to evaluate different SFE tasks without the need for reprogramming and fully explores the parallelism for any GC problem. Our system demonstrates an order of magnitude speedup compared with an existing software platform.
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Sancho, Jorge, José García, and Álvaro Alesanco. "Oblivious Inspection: On the Confrontation between System Security and Data Privacy at Domain Boundaries." Security and Communication Networks 2020 (September 22, 2020): 1–9. http://dx.doi.org/10.1155/2020/8856379.
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In this work, we introduce the system boundary security vs. privacy dilemma, where border devices (e.g., firewall devices) require unencrypted data inspection to prevent data exfiltration or unauthorized data accesses, but unencrypted data inspection violates data privacy. To shortcut this problem, we present Oblivious Inspection, a novel approach based on garbled circuits to perform a stateful application-aware inspection of encrypted network traffic in a privacy-preserving way. We also showcase an inspection algorithm for Fast Healthcare Interoperability Resources (FHIR) standard compliant packets along with its performance results. The results point out the importance of the inspection function being aligned with the underlying garbled circuit protocol. In this line, mandatory encryption algorithms for TLS 1.3 have been analysed observing that packets encrypted using Chacha20 can be filtered up to 17 and 25 times faster compared with AES128-GCM and AES256-GCM, respectively. All together, this approach penalizes performance to align system security and data privacy, but it could be appropriate for those scenarios where this performance degradation can be justified by the sensibility of the involved data such as healthcare scenarios.
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Li, Mengxing, Quan Feng, Jian Zhao, Mei Yang, Lijun Kang, and Lili Wu. "Minutiae Matching with Privacy Protection Based on the Combination of Garbled Circuit and Homomorphic Encryption." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/525387.
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Biometrics plays an important role in authentication applications since they are strongly linked to holders. With an increasing growth of e-commerce and e-government, one can expect that biometric-based authentication systems are possibly deployed over the open networks in the near future. However, due to its openness, the Internet poses a great challenge to the security and privacy of biometric authentication. Biometric data cannot be revoked, so it is of paramount importance that biometric data should be handled in a secure way. In this paper we present a scheme achieving privacy-preserving fingerprint authentication between two parties, in which fingerprint minutiae matching algorithm is completed in the encrypted domain. To improve the efficiency, we exploit homomorphic encryption as well as garbled circuits to design the protocol. Our goal is to provide protection for the security of template in storage and data privacy of two parties in transaction. The experimental results show that the proposed authentication protocol runs efficiently. Therefore, the protocol can run over open networks and help to alleviate the concerns on security and privacy of biometric applications over the open networks.
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Mohassel, Payman, Mike Rosulek, and Ni Trieu. "Practical Privacy-Preserving K-means Clustering." Proceedings on Privacy Enhancing Technologies 2020, no. 4 (October 1, 2020): 414–33. http://dx.doi.org/10.2478/popets-2020-0080.
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AbstractClustering is a common technique for data analysis, which aims to partition data into similar groups. When the data comes from different sources, it is highly desirable to maintain the privacy of each database. In this work, we study a popular clustering algorithm (K-means) and adapt it to the privacypreserving context.Specifically, to construct our privacy-preserving clustering algorithm, we first propose an efficient batched Euclidean squared distance computation protocol in the amortizing setting, when one needs to compute the distance from the same point to other points. Furthermore, we construct a customized garbled circuit for computing the minimum value among shared values.We believe these new constructions may be of independent interest. We implement and evaluate our protocols to demonstrate their practicality and show that they are able to train datasets that are much larger and faster than in the previous work. The numerical results also show that the proposed protocol achieve almost the same accuracy compared to a K-means plain-text clustering algorithm.
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Tueno, Anselme, Florian Kerschbaum, and Stefan Katzenbeisser. "Private Evaluation of Decision Trees using Sublinear Cost." Proceedings on Privacy Enhancing Technologies 2019, no. 1 (January 1, 2019): 266–86. http://dx.doi.org/10.2478/popets-2019-0015.
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Abstract Decision trees are widespread machine learning models used for data classification and have many applications in areas such as healthcare, remote diagnostics, spam filtering, etc. In this paper, we address the problem of privately evaluating a decision tree on private data. In this scenario, the server holds a private decision tree model and the client wants to classify its private attribute vector using the server’s private model. The goal is to obtain the classification while preserving the privacy of both – the decision tree and the client input. After the computation, only the classification result is revealed to the client, while nothing is revealed to the server. Many existing protocols require a constant number of rounds. However, some of these protocols perform as many comparisons as there are decision nodes in the entire tree and others transform the whole plaintext decision tree into an oblivious program, resulting in higher communication costs. The main idea of our novel solution is to represent the tree as an array. Then we execute only d – the depth of the tree – comparisons. Each comparison is performed using a small garbled circuit, which output secret-shares of the index of the next node. We get the inputs to the comparison by obliviously indexing the tree and the attribute vector. We implement oblivious array indexing using either garbled circuits, Oblivious Transfer or Oblivious RAM (ORAM). Using ORAM, this results in the first protocol with sub-linear cost in the size of the tree. We implemented and evaluated our solution using the different array indexing procedures mentioned above. As a result, we are not only able to provide the first protocol with sublinear cost for large trees, but also reduce the communication cost for the large real-world data set “Spambase” from 18 MB to 1[triangleright]2 MB and the computation time from 17 seconds to less than 1 second in a LAN setting, compared to the best related work.
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Kiss, Ágnes, Jian Liu, Thomas Schneider, N. Asokan, and Benny Pinkas. "Private Set Intersection for Unequal Set Sizes with Mobile Applications." Proceedings on Privacy Enhancing Technologies 2017, no. 4 (October 1, 2017): 177–97. http://dx.doi.org/10.1515/popets-2017-0044.
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Abstract Private set intersection (PSI) is a cryptographic technique that is applicable to many privacy-sensitive scenarios. For decades, researchers have been focusing on improving its efficiency in both communication and computation. However, most of the existing solutions are inefficient for an unequal number of inputs, which is common in conventional client-server settings. In this paper, we analyze and optimize the efficiency of existing PSI protocols to support precomputation so that they can efficiently deal with such input sets. We transform four existing PSI protocols into the precomputation form such that in the setup phase the communication is linear only in the size of the larger input set, while in the online phase the communication is linear in the size of the smaller input set. We implement all four protocols and run experiments between two PCs and between a PC and a smartphone and give a systematic comparison of their performance. Our experiments show that a protocol based on securely evaluating a garbled AES circuit achieves the fastest setup time by several orders of magnitudes, and the fastest online time in the PC setting where AES-NI acceleration is available. In the mobile setting, the fastest online time is achieved by a protocol based on the Diffie-Hellman assumption.
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Riazi, M. Sadegh, Ebrahim M. Songhori, Ahmad-Reza Sadeghi, Thomas Schneider, and Farinaz Koushanfar. "Toward Practical Secure Stable Matching." Proceedings on Privacy Enhancing Technologies 2017, no. 1 (January 1, 2017): 62–78. http://dx.doi.org/10.1515/popets-2017-0005.
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Abstract The Stable Matching (SM) algorithm has been deployed in many real-world scenarios including the National Residency Matching Program (NRMP) and financial applications such as matching of suppliers and consumers in capital markets. Since these applications typically involve highly sensitive information such as the underlying preference lists, their current implementations rely on trusted third parties. This paper introduces the first provably secure and scalable implementation of SM based on Yao’s garbled circuit protocol and Oblivious RAM (ORAM). Our scheme can securely compute a stable match for 8k pairs four orders of magnitude faster than the previously best known method. We achieve this by introducing a compact and efficient sub-linear size circuit. We even further decrease the computation cost by three orders of magnitude by proposing a novel technique to avoid unnecessary iterations in the SM algorithm. We evaluate our implementation for several problem sizes and plan to publish it as open-source.
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Kim, Yong-Ki, Hyeong-Jin Kim, Hyunjo Lee, and Jae-Woo Chang. "Privacy-preserving parallel kNN classification algorithm using index-based filtering in cloud computing." PLOS ONE 17, no. 5 (May 5, 2022): e0267908. http://dx.doi.org/10.1371/journal.pone.0267908.
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With the development of cloud computing, interest in database outsourcing has recently increased. In cloud computing, it is necessary to protect the sensitive information of data owners and authorized users. For this, data mining techniques over encrypted data have been studied to protect the original database, user queries and data access patterns. The typical data mining technique is kNN classification which is widely used for data analysis and artificial intelligence. However, existing works do not provide a sufficient level of efficiency for a large amount of encrypted data. To solve this problem, in this paper, we propose a privacy-preserving parallel kNN classification algorithm. To reduce the computation cost for encryption, we propose an improved secure protocol by using an encrypted random value pool. To reduce the query processing time, we not only design a parallel algorithm, but also adopt a garbled circuit. In addition, the security analysis of the proposed algorithm is performed to prove its data protection, query protection, and access pattern protection. Through our performance evaluation, the proposed algorithm shows about 2∼25 times better performance compared with existing algorithms.
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Huang, Junxin, Yuchuan Luo, Ming Xu, Bowen Hu, and Jian Long. "pShare: Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route." Applied Sciences 12, no. 2 (January 14, 2022): 842. http://dx.doi.org/10.3390/app12020842.
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Online ride-hailing (ORH) services allow people to enjoy on-demand transportation services through their mobile devices in a short responding time. Despite the great convenience, users need to submit their location information to the ORH service provider, which may incur unexpected privacy problems. In this paper, we mainly study the privacy and utility of the ride-sharing system, which enables multiple riders to share one driver. To solve the privacy problem and reduce the ride-sharing detouring waste, we propose a privacy-preserving ride-sharing system named pShare. To hide users’ precise locations from the service provider, we apply a zone-based travel time estimation approach to privately compute over sensitive data while cloaking each rider’s location in a zone area. To compute the matching results along with the least-detouring route, the service provider first computes the shortest path for each eligible rider combination, then compares the additional traveling time (ATT) of all combinations, and finally selects the combination with minimum ATT. We designed a secure comparing protocol by utilizing the garbled circuit, which enables the ORH server to execute the protocol with a crypto server without privacy leakage. Moreover, we apply the data packing technique, by which multiple data can be packed as one to reduce the communication and computation overhead. Through the theoretical analysis and evaluation results, we prove that pShare is a practical ride-sharing scheme that can find out the sharing riders with minimum ATT in acceptable accuracy while protecting users’ privacy.
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Zhang, Liang Feng, and Reihaneh Safavi-Naini. "Privacy-preserving verifiable delegation of polynomial and matrix functions." Journal of Mathematical Cryptology 14, no. 1 (July 3, 2020): 153–71. http://dx.doi.org/10.1515/jmc-2018-0039.
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AbstractOutsourcing computation has gained significant popularity in recent years due to the development of cloud computing and mobile services. In a basic outsourcing model, a client delegates computation of a function f on an input x to a server. There are two main security requirements in this setting: guaranteeing the server performs the computation correctly, and protecting the client’s input (and hence the function value) from the server. The verifiable computation model of Gennaro, Gentry and Parno achieves the above requirements, but the resulting schemes lack efficiency. This is due to the use of computationally expensive primitives such as fully homomorphic encryption (FHE) and garbled circuits, and the need to represent f as a Boolean circuit. Also, the security model does not allow verification queries, which implies the server cannot learn if the client accepts the computation result. This is a weak security model that does not match many real life scenarios. In this paper, we construct efficient (i.e., without using FHE, garbled circuits and Boolean circuit representations) verifiable computation schemes that provide privacy for the client’s input, and prove their security in a strong model that allows verification queries. We first propose a transformation that provides input privacy for a number of existing schemes for verifiable delegation of multivariate polynomial f over a finite field. Our transformation is based on noisy encoding of x and keeps x semantically secure under the noisy curve reconstruction (CR) assumption. We then propose a construction for verifiable delegation of matrix-vector multiplication, where the delegated function f is a matrix and the input to the function is a vector. The scheme uses PRFs with amortized closed-form efficiency and achieves high efficiency. We outline applications of our results to outsourced two-party protocols.
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Gascón, Adrià, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur, and David Evans. "Privacy-Preserving Distributed Linear Regression on High-Dimensional Data." Proceedings on Privacy Enhancing Technologies 2017, no. 4 (October 1, 2017): 345–64. http://dx.doi.org/10.1515/popets-2017-0053.
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Abstract We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD) algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.’s method for privacy-preserving ridge regression (S&P 2013), and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time.
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Almashaqbeh, Ghada, Fabrice Benhamouda, Seungwook Han, Daniel Jaroslawicz, Tal Malkin, Alex Nicita, Tal Rabin, Abhishek Shah, and Eran Tromer. "Gage MPC: Bypassing Residual Function Leakage for Non-Interactive MPC." Proceedings on Privacy Enhancing Technologies 2021, no. 4 (July 23, 2021): 528–48. http://dx.doi.org/10.2478/popets-2021-0083.
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Abstract Existing models for non-interactive MPC cannot provide full privacy for inputs, because they inherently leak the residual function (i.e., the output of the function on the honest parties’ input together with all possible values of the adversarial inputs). For example, in any non-interactive sealed-bid auction, the last bidder can figure out what was the highest previous bid. We present a new MPC model which avoids this privacy leak. To achieve this, we utilize a blockchain in a novel way, incorporating smart contracts and arbitrary parties that can be incentivized to perform computation (“bounty hunters,” akin to miners). Security is maintained under a monetary assumption about the parties: an honest party can temporarily supply a recoverable collateral of value higher than the computational cost an adversary can expend. We thus construct non-interactive MPC protocols with strong security guarantees (full security, no residual leakage) in the short term. Over time, as the adversary can invest more and more computational resources, the security guarantee decays. Thus, our model, which we call Gage MPC, is suitable for secure computation with limited-time secrecy, such as auctions. A key ingredient in our protocols is a primitive we call “Gage Time Capsules” (GaTC): a time capsule that allows a party to commit to a value that others are able to reveal but only at a designated computational cost. A GaTC allows a party to commit to a value together with a monetary collateral. If the original party properly opens the GaTC, it can recover the collateral. Otherwise, the collateral is used to incentivize bounty hunters to open the GaTC. This primitive is used to ensure completion of Gage MPC protocols on the desired inputs. As a requisite tool (of independent interest), we present a generalization of garbled circuit that are more robust: they can tolerate exposure of extra input labels. This is in contrast to Yao’s garbled circuits, whose secrecy breaks down if even a single extra label is exposed. Finally, we present a proof-of-concept implementation of a special case of our construction, yielding an auction functionality over an Ethereum-like blockchain.
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Li, Ye, Zoe L. Jiang, Xuan Wang, Junbin Fang, En Zhang, and Xianmin Wang. "Securely Outsourcing ID3 Decision Tree in Cloud Computing." Wireless Communications and Mobile Computing 2018 (October 4, 2018): 1–10. http://dx.doi.org/10.1155/2018/2385150.
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With the wide application of Internet of Things (IoT), a huge number of data are collected from IoT networks and are required to be processed, such as data mining. Although it is popular to outsource storage and computation to cloud, it may invade privacy of participants’ information. Cryptography-based privacy-preserving data mining has been proposed to protect the privacy of participating parties’ data for this process. However, it is still an open problem to handle with multiparticipant’s ciphertext computation and analysis. And these algorithms rely on the semihonest security model which requires all parties to follow the protocol rules. In this paper, we address the challenge of outsourcing ID3 decision tree algorithm in the malicious model. Particularly, to securely store and compute private data, the two-participant symmetric homomorphic encryption supporting addition and multiplication is proposed. To keep from malicious behaviors of cloud computing server, the secure garbled circuits are adopted to propose the privacy-preserving weight average protocol. Security and performance are analyzed.
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Kim, Hyeong-Jin, Hyunjo Lee, Yong-Ki Kim, and Jae-Woo Chang. "Privacy-preserving kNN query processing algorithms via secure two-party computation over encrypted database in cloud computing." Journal of Supercomputing 78, no. 7 (January 17, 2022): 9245–84. http://dx.doi.org/10.1007/s11227-021-04286-2.
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AbstractSince studies on privacy-preserving database outsourcing have been spotlighted in a cloud computing, databases need to be encrypted before being outsourced to the cloud. Therefore, a couple of privacy-preserving kNN query processing algorithms have been proposed over the encrypted database. However, the existing algorithms are either insecure or inefficient. Therefore, in this paper we propose a privacy-preserving kNN query processing algorithm via secure two-party computation on the encrypted database. Our algorithm preserves both data privacy and query privacy while hiding data access patterns. For this, we propose efficient and secure protocols based on Yao’s garbled circuit. To achieve a high degree of efficiency in query processing, we also propose a parallel kNN query processing algorithm using encrypted random value pool. Through our performance analysis, we verify that our proposed algorithms outperform the existing ones in terms of a query processing cost.
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Deuber, Dominic, Christoph Egger, Katharina Fech, Giulio Malavolta, Dominique Schröder, Sri Aravinda Krishnan Thyagarajan, Florian Battke, and Claudia Durand. "My Genome Belongs to Me: Controlling Third Party Computation on Genomic Data." Proceedings on Privacy Enhancing Technologies 2019, no. 1 (January 1, 2019): 108–32. http://dx.doi.org/10.2478/popets-2019-0007.
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Abstract An individual’s genetic information is possibly the most valuable personal information. While knowledge of a person’s DNA sequence can facilitate the diagnosis of several heritable diseases and allow personalized treatment, its exposure comes with significant threats to the patient’s privacy. Currently known solutions for privacy-respecting computation require the owner of the DNA to either be heavily involved in the execution of a cryptographic protocol or to completely outsource the access control to a third party. This motivates the demand for cryptographic protocols which enable computation over encrypted genomic data while keeping the owner of the genome in full control. We envision a scenario where data owners can exercise arbitrary and dynamic access policies, depending on the intended use of the analysis results and on the credentials of who is conducting the analysis. At the same time, data owners are not required to maintain a local copy of their entire genetic data and do not need to exhaust their computational resources in an expensive cryptographic protocol. In this work, we present METIS, a system that assists the computation over encrypted data stored in the cloud while leaving the decision on admissible computations to the data owner. It is based on garbled circuits and supports any polynomially-computable function. A critical feature of our system is that the data owner is free from computational overload and her communication complexity is independent of the size of the input data and only linear in the size of the circuit’s output. We demonstrate the practicality of our approach with an implementation and an evaluation of several functions over real datasets.
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Wagh, Sameer, Divya Gupta, and Nishanth Chandran. "SecureNN: 3-Party Secure Computation for Neural Network Training." Proceedings on Privacy Enhancing Technologies 2019, no. 3 (July 1, 2019): 26–49. http://dx.doi.org/10.2478/popets-2019-0035.
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Abstract Neural Networks (NN) provide a powerful method for machine learning training and inference. To effectively train, it is desirable for multiple parties to combine their data – however, doing so conflicts with data privacy. In this work, we provide novel three-party secure computation protocols for various NN building blocks such as matrix multiplication, convolutions, Rectified Linear Units, Maxpool, normalization and so on. This enables us to construct three-party secure protocols for training and inference of several NN architectures such that no single party learns any information about the data. Experimentally, we implement our system over Amazon EC2 servers in different settings. Our work advances the state-of-the-art of secure computation for neural networks in three ways: 1. Scalability: We are the first work to provide neural network training on Convolutional Neural Networks (CNNs) that have an accuracy of > 99% on the MNIST dataset; 2. Performance: For secure inference, our system outperforms prior 2 and 3-server works (SecureML, MiniONN, Chameleon, Gazelle) by 6×-113× (with larger gains obtained in more complex networks). Our total execution times are 2 − 4× faster than even just the online times of these works. For secure training, compared to the only prior work (SecureML) that considered a much smaller fully connected network, our protocols are 79× and 7× faster than their 2 and 3-server protocols. In the WAN setting, these improvements are more dramatic and we obtain an improvement of 553×! 3. Security: Our protocols provide two kinds of security: full security (privacy and correctness) against one semi-honest corruption and the notion of privacy against one malicious corruption [Araki et al. CCS’16]. All prior works only provide semi-honest security and ours is the first system to provide any security against malicious adversaries for the secure computation of complex algorithms such as neural network inference and training. Our gains come from a significant improvement in communication through the elimination of expensive garbled circuits and oblivious transfer protocols.
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"Private Trajectory Intersection Testing: Is Garbled Circuit Better than Custom Protocols?" International Journal of Engineering 34, no. 4 (2021). http://dx.doi.org/10.5829/ije.2021.34.04a.12.
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De Cock, Martine, Rafael Dowsley, Anderson C. A. Nascimento, Davis Railsback, Jianwei Shen, and Ariel Todoki. "High performance logistic regression for privacy-preserving genome analysis." BMC Medical Genomics 14, no. 1 (January 20, 2021). http://dx.doi.org/10.1186/s12920-020-00869-9.
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Abstract Background In biomedical applications, valuable data is often split between owners who cannot openly share the data because of privacy regulations and concerns. Training machine learning models on the joint data without violating privacy is a major technology challenge that can be addressed by combining techniques from machine learning and cryptography. When collaboratively training machine learning models with the cryptographic technique named secure multi-party computation, the price paid for keeping the data of the owners private is an increase in computational cost and runtime. A careful choice of machine learning techniques, algorithmic and implementation optimizations are a necessity to enable practical secure machine learning over distributed data sets. Such optimizations can be tailored to the kind of data and Machine Learning problem at hand. Methods Our setup involves secure two-party computation protocols, along with a trusted initializer that distributes correlated randomness to the two computing parties. We use a gradient descent based algorithm for training a logistic regression like model with a clipped ReLu activation function, and we break down the algorithm into corresponding cryptographic protocols. Our main contributions are a new protocol for computing the activation function that requires neither secure comparison protocols nor Yao’s garbled circuits, and a series of cryptographic engineering optimizations to improve the performance. Results For our largest gene expression data set, we train a model that requires over 7 billion secure multiplications; the training completes in about 26.90 s in a local area network. The implementation in this work is a further optimized version of the implementation with which we won first place in Track 4 of the iDASH 2019 secure genome analysis competition. Conclusions In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure multi-party computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.
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Ong, Toan, Ibrahim Lazrig, Indrajit Ray, Indrakshi Ray, and Michael Kahn. "Scalable Secure Privacy-Preserving Record Linkage (PPRL) Methods Using Cloud-based Infrastructure." International Journal of Population Data Science 3, no. 4 (August 23, 2018). http://dx.doi.org/10.23889/ijpds.v3i4.638.
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IntroductionBloom Filters (BFs) are a scalable solution for probabilistic privacy-preserving record linkage but BFs can be compromised. Yao’s garbled circuits (GCs) can perform secure multi-party computation to compute the similarity of two BFs without a trusted third party. The major drawback of using BFs and GCs together is poor efficiency.
Objectives and ApproachWe evaluated the feasibility of BFs+GCs using high capacity compute engines and implementing a novel parallel processing framework in Google Cloud Compute Engines (GCCE). In the Yao’s two-party secure computation protocol, one party serves as the generator and the other party serves as the evaluator. To link data in parallel, records from both parties are divided into chunks. Linkage between every two chunks in the same block is processed by a thread. The number of threads for linkage depends on available computing resources. We tested the parallelized process in various scenarios with variations in hardware and software configurations.
ResultsTwo synthetic datasets with 10K records were linked using BFs+GCs on 12 different software and hardware configurations which varied by: number of CPU cores (4 to 32), memory size (15GB – 28.8GB), number of threads (6-41), and chunk size (50-200 records). The minimum configuration (4 cores; 15GB memory) took 8,062.4s to complete whereas the maximum configuration (32 cores; 28.8GB memory) took 1,454.1s. Increasing the number of threads or changing the chunk size without providing more CPU cores and memory did not improve the efficiency. Efficiency is improved on average by 39.81% when the number of cores and memory on the both sides are doubled. The CPU utilization is maximized (near 100% on both sides) when the computing power of the generator is double the evaluator.
Conclusion/ImplicationsThe PPRL runtime of BFs+GCs was greatly improved using parallel processing in a cloud-based infrastructure. A cluster of GCCEs could be leveraged to reduce the runtime of data linkage operations even further. Scalable cloud-based infrastructures can overcome the trade-off between security and efficiency, allowing computationally complex methods to be implemented.