Privacy-preserving Outsourcing Research Articles

Efficient and Privacy-Preserving Outsourcing of Gradient Boosting Decision Tree Inference

Efficient and Privacy-Preserving Outsourcing of Gradient Boosting Decision Tree Inference

SDIM: A Subtly Designed Invertible Matrix for Enhanced Privacy-Preserving Outsourcing Matrix Multiplication and Related Tasks

SDIM: A Subtly Designed Invertible Matrix for Enhanced Privacy-Preserving Outsourcing Matrix Multiplication and Related Tasks

Privacy-preserving outsourcing scheme of face recognition based on locally linear embedding

Face recognition is a biometric-based technology that identifies or verifies a person’s identity by analyzing and comparing patterns in their facial features. Among kinds of face recognition algorithms, Locally Linear Embedding (LLE) algorithm has the advantage of preserving the local structure of face data. However, the LLE algorithm requires some computationally intensive matrix calculations, which might introduce latency to the device. In this paper, we propose a privacy-preserving outsourcing scheme of face recognition based on LLE algorithm for the first time. By outsourcing matrix multiplication, linear equations, and eigenvalue decomposition of the LLE algorithm to a cloud server, we reduce the computational load on the client device. To ensure data security, we generate a novel type of sparse orthogonal matrix through square root application and random permutation of diagonal elements, used as secret keys to blind the client’s data. Meanwhile, due to the sparsity of key matrices, the complexity of client-side computation complexity is reduced from O(n3) to O(n2). The experimental results show that our proposed scheme reduces computational costs while maintaining almost identical face recognition accuracy as the original LLE-based algorithm.

Privacy-Preserving Outsourcing of K-Means Clustering for Cloud-Device Collaborative Computing in Space-Air-Ground Integrated IoT

Facing the explosive growth of data, the introduction of cloud computing in the space-air-ground integrated Internet of things (SAGIIoT) can solve the problem of limited computing power of the terminals. At the same time, data security on the cloud is also a focus that cannot be ignored. Secure outsourcing computing is helpful in improving privacy-preserving. Due to the wide applicability of K-means clustering, outsourcing computing for K-means has become a major research hotspot in industry and academia. Most of the existing work on outsourcing K-means clustering is based on homomorphic encryption, which has a high computational overhead due to the mathematical puzzles’ nature of homomorphic encryption. In addition, the high computational overhead of designing a verification algorithm based on homomorphic encryption is unacceptable. To address the above issues, we design a K-means clustering outsourcing algorithm by sparse matrix transformation, which can verify the deceptive behavior of cloud while achieving high efficiency. In this paper, we theoretically prove the accuracy, security, efficiency and verifiability of the proposed algorithm. Extensive experiments indicate that our algorithm is efficient.

Privacy-Preserving Outsourcing Algorithm for Solving Large Systems of Linear Equations

Privacy-Preserving Outsourcing Algorithm for Solving Large Systems of Linear Equations

Privacy-preserving outsourcing decision tree evaluation from homomorphic encryption

A decision tree is a common algorithm in machine learning, which performs classification prediction based on a tree structure. In real-world scenarios, input attribute values may be sensitive and tree models are usually private, thus the computation potentially incurs security and privacy risks. In this paper, we focus on how to retain privacy when outsourcing decision tree evaluation. Leveraging homomorphic encryption, our construction achieves data confidentiality against semi-honest adversaries while enabling both the client (who provides the attributes) and the model holder to be offline during evaluation. Based on an unbalanced-computational-ability two-server model, we achieve single-branch evaluation, which is exponentially less computation than previous schemes that evaluated the entire decision tree. A proof-of-concept implementation demonstrates this. Additionally, with multi-key encryption and joint decryption, our protocol easily supports multi-client scenarios.

A Privacy-Preserving Outsourcing Computing Scheme Based on Secure Trusted Environment

As one of the key technologies to enable the internet of things (IoT), cloud computing plays a significant role in providing huge computing and storage facilities for large-scale data. Though cloud computing brings great advantages, new issues emerge, such as data security breach and privacy disclosure. In this paper, we introduce a novel secure and privacy-preserving outsourcing computing scheme (hereafter referred to as SPOCS) to tackle this issue. In SPOCS, the effective use of Intel SGX, one of the trusted execution environment (TEE), ensures the confidence and integrity of sensitive data in cloud computing and prevents data loss from causing privacy disclosure. In order to keep malicious cloud service providers (CSPs) from illegally tampering with the outsourcing results, blockchain is employed to ensure the data immutability. Significantly, our proposed scheme achieves anonymity and traceability. In the outsourcing process, smart contracts are applied to make the whole process fully automated without any human involvement. Finally, the security of the proposed scheme is analyzed in terms of its resistance to different attacks. The experiments indicate that our scheme is effective and efficient.

Toward Correlated Data Trading for Private Web Browsing History

The trading of social media data has attracted wide research interests over years. In particular the trading for web browsing histories, when being applied to targeted advertising, produces tremendous economic value for data consumers. However, the disclosure of entire browsing histories, even in form of anonymous datasets, poses a huge threat to user privacy. Although some existing solutions have investigated privacy-preserving outsourcing of social media data, unfortunately, they neglected the impact on the data consumer’s utility. In this paper, we propose CEATSE, a CorrElated dAta Trading framework for various kinds of private web browSing historiEs. CEATSE first models the correlation among multiple dimensional features, and then generates the optimal feature clustering scheme. Combined with this scheme, CEATSE next incorporates a correlated data perturbation strategy on each feature cluster, in order to balance the privacy-utility tradeoff. It then quantifies each chosen data contributor’s privacy loss on optimal feature clusters. Through real-data based experiments, our analysis and evaluation results demonstrate CEATSE indeed achieves user privacy protection, the data consumer’s accuracy requirement, and truthfulness, individual rationality as well as budget balance.

POMIC: Privacy-Preserving Outsourcing Medical Image Classification Based on Convolutional Neural Network to Cloud

In the medical field, with the increasing number of medical images, medical image classification has become a hot spot. The convolutional neural network, a technology that can process more images and extract more accurate features with nonlinear models, has been widely used in this field. However, the classification process with model training with existing medical images needs a large number of samples, and the operation involves complex parameter computations, which puts forward higher requirements for users. Therefore, we propose a scheme for flexible privacy-preserving outsourcing medical image classification based on a convolutional neural network to the cloud. In this paper, three servers on the cloud platform can train the model with images from users, but they cannot obtain complete information on model parameters and user input. In practice, the scheme can not only reduce the computation and storage burdens on the user side but also ensure the security and efficiency of the system, which can be confirmed through the implementation of the experiment.

PP-DDP: a privacy-preserving outsourcing framework for solving the double digest problem

BackgroundAs one of the fundamental problems in bioinformatics, the double digest problem (DDP) focuses on reordering genetic fragments in a proper sequence. Although many algorithms for dealing with the DDP problem were proposed during the past decades, it is believed that solving DDP is still very time-consuming work due to the strongly NP-completeness of DDP. However, none of these algorithms consider the privacy issue of the DDP data that contains critical business interests and is collected with days or even months of gel-electrophoresis experiments. Thus, the DDP data owners are reluctant to deploy the task of solving DDP over cloud.ResultsOur main motivation in this paper is to design a secure outsourcing computation framework for solving the DDP problem. We at first propose a privacy-preserving outsourcing framework for handling the DDP problem by using a cloud server; Then, to enable the cloud server to solve the DDP instances over ciphertexts, an order-preserving homomorphic index scheme (OPHI) is tailored from an order-preserving encryption scheme published at CCS 2012; And finally, our previous work on solving DDP problem, a quantum inspired genetic algorithm (QIGA), is merged into our outsourcing framework, with the supporting of the proposed OPHI scheme. Moreover, after the execution of QIGA at the cloud server side, the optimal solution, i.e. two mapping sequences, would be transferred publicly to the data owner. Security analysis shows that from these sequences, none can learn any information about the original DDP data. Performance analysis shows that the communication cost and the computational workload for both the client side and the server side are reasonable. In particular, our experiments show that PP-DDP can find optional solutions with a high success rate towards typical test DDP instances and random DDP instances, and PP-DDP takes less running time than DDmap, SK05 and GM12, while keeping the privacy of the original DDP data.ConclusionThe proposed outsourcing framework, PP-DDP, is secure and effective for solving the DDP problem.

Reconfigurable multi-core array architecture and mapping method for RNS-based homomophic encryption

Fully homomorphic encryption (FHE) plays a vital role in privacy-preserving outsourcing computing and cloud computing security. However, efficiency is still the main factor limiting the actual use of FHE. This paper presents an area-efficient reconfigurable multi-core array architecture (named RMCA) and mapping method for the RNS variant of CKKS scheme. To accelerate the time-consuming polynomial multiplication, we present an improved NTT/INTT algorithm without pre/post-processing and a reconfigurable processing element (PE) unit that can be configured as NTT, INTT and modular multiplier. Also, a memory-saving NTT architecture and memory organization of the twiddle factor are introduced to reduce the data and twiddle factor memory overhead by 25 % and 50 %, respectively. Furthermore, targeting the computational requirements of RNS-CKKS, a unified computational model and distributed on-chip memory organization are presented for RMCA. Lastly, all the computational units involved in the homomorphic evaluation of RNS-CKKS are optimized and mapped on RMCA reasonably. When evaluated on Virtex UltraScale XCVU190 FPGA at 300 MHz, RMCA can perform 9154, 4308 and 1743 homomorphic multiplications per second for N = 4096, 8192 and 16384, respectively, and the area-time-products (ATPs) are improved by 1.11×∼8.60 × for a wide range of parameter sets.

Medha: Microcoded Hardware Accelerator for computing on Encrypted Data

Homomorphic encryption enables computation on encrypted data, and hence it has a great potential in privacy-preserving outsourcing of computations to the cloud. Hardware acceleration of homomorphic encryption is crucial as software implementations are very slow. In this paper, we present design methodologies for building a programmable hardware accelerator for speeding up the cloud-side homomorphic evaluations on encrypted data.First, we propose a divide-and-conquer technique that enables homomorphic evaluations in the polynomial ring RQ,2N = ZQ[x]/(x2N + 1) to use a hardware accelerator that has been built for the smaller ring RQ,N = ZQ[x]/(xN + 1). The technique makes it possible to use a single hardware accelerator flexibly for supporting several homomorphic encryption parameter sets.Next, we present several architectural design methods that we use to realize the flexible and instruction-set accelerator architecture, which we call ‘Medha’. At every level of the implementation hierarchy, we explore possibilities for parallel processing. Starting from hardware-friendly parallel algorithms for the basic building blocks, we gradually build heavily parallel RNS polynomial arithmetic units. Next, many of these parallel units are interconnected elegantly so that their interconnections require the minimum number of nets, therefore making the overall architecture placement-friendly on the platform. As homomorphic encryption is computation- as well as data-centric, the speed of homomorphic evaluations depends greatly on the way the data variables are handled. For Medha, we take a memory-conservative design approach and get rid of any off-chip memory access during homomorphic evaluations.Finally, we implement Medha in a Xilinx Alveo U250 FPGA and measure timing performances of the microcoded homomorphic addition, multiplication, key-switching, and rescaling routines for the leveled fully homomorphic encryption scheme RNSHEAAN at 200 MHz clock frequency. For the large parameter sets (log Q,N) = (438, 214) and (546, 215), Medha achieves accelerations by up to 68× and 78× times respectively compared to a highly optimized software implementation Microsoft SEAL running at 2.3 GHz.

Enabling Privacy-Preserving Parallel Outsourcing Matrix Inversion in IoT

With the rapid development of Internet of Things (IoT), edge computing has been widely applied as a novel computing paradigm. Securely outsourcing intensive tasks to edge servers is becoming increasingly pervasive. It is a nice approach for resource-limited IoT devices to accomplish heavy computing tasks. Matrix inversion is a basic but time-consuming operation, which has a wide range of applications in IoT. The current privacy-preserving outsourcing schemes for matrix inversion cannot support parallel computing based on multiple edge servers. As a result, they cannot well satisfy the requirement of fast response for computation in IoT. In order to deal with this problem, we propose two privacy-preserving parallel outsourcing schemes for matrix inversion in IoT. In the first scheme, we design a novel method to generate a random matrix, which is used to blind the inputted original matrix. In this scheme, two edge servers compute the inversion of the encrypted matrix in parallel to improve the computational efficiency. To further improve the efficiency, we design a novel subtasks partitioning and assignment strategy and propose the second scheme by balancing the computing load of edge servers. We analyze the correctness, security, and verifiability of the proposed schemes. And we provide theoretical analysis and experimental results to demonstrate the performance advantages of the proposed schemes.

Privacy-Preserving Outsourcing Scheme for SVM on Vertically Partitioned Data

Support vector machine (SVM) is an important technique for data classification. Traditional SVM assumes free access to data. If the data are split and held by different users, for privacy reasons, users are likely unwilling to submit their data to a third party for classification. In this paper, by using additive homomorphic encryption and random transformations (matrix transformation and vector decomposition), we design a privacy-preserving outsourcing scheme for conducting Least Squares SVM (LS-SVM) classification on vertically partitioned data. In our system, multiple data owners (users) submit their encrypted data to two non-colluding service providers, which conduct SVM algorithm on it. During the execution of our algorithm, neither service provider learns anything about the input data, the intermediate results, or the predicted result. In other words, our algorithm is encrypted in the whole process. Extensive theoretical analysis and experimental evaluation demonstrate the correctness, security, and efficiency of the method.

Privacy-Preserving Outsourcing Algorithms for Multidimensional Data Encryption in Smart Grids.

With the development of the Internet of Things, smart grids have become indispensable in our daily life and can provide people with reliable electricity generation, transmission, distribution and control. Therefore, how to design a privacy-preserving data aggregation protocol has been a research hot-spot in smart grid technology. However, these proposed protocols often contain some complex cryptographic operations, which are not suitable for resource-constrained smart meter devices. In this paper, we combine data aggregation and the outsourcing of computations to design two privacy-preserving outsourcing algorithms for the modular exponentiation operations involved in the multi-dimensional data aggregation, which can allow these smart meter devices to delegate complex computation tasks to nearby servers for computing. By utilizing our proposed outsourcing algorithms, the computational overhead of resource-constrained smart meter devices can be greatly reduced in the process of data encryption and aggregation. In addition, the proposed algorithms can protect the input’s privacy of smart meter devices and ensure that the smart meter devices can verify the correctness of results from the server with a very small computational cost. From three aspects, including security, verifiability and efficiency, we give a detailed analysis about our proposed algorithms. Finally, through carrying out some experiments, we prove that our algorithms can improve the efficiency of performing the data encryption and aggregation on the smart meter device side.

Privacy-Preserving and Verifiable Outsourcing Message Transmission and Authentication Protocol in Iot with Edge Computing

Privacy-Preserving and Verifiable Outsourcing Message Transmission and Authentication Protocol in Iot with Edge Computing

Secure and Evaluable Clustering Based on a Multifunctional and Privacy-Preserving Outsourcing Computation Toolkit

Although tremendous revolution has been made in the emerging cloud computing technologies over digital devices, privacy gradually becomes a big concern in outsourcing computation. Homomorphic encryption has been proposed to facilitate the preservation of data privacy while computational tasks being executed on ciphertext. However, many existing studies only support limited homomorphic calculation functions which barely satisfy complex computing tasks such as machine learning with massive computing resources and rich types of function. To address this problem, a novel multifunctional and privacy-preserving outsourcing computation toolkit is proposed in this paper, which supports several homomorphic computing protocols including division and power on ciphertext of integers and floating point numbers. Specifically, we first implement the homomorphic mutual conversion protocol between integer and floating point ciphertext to balance the efficiency and feasibility, considering the high-precision ciphertext operation on floating point numbers costs 100x computational overhead than that on integers. Second, we implement a homomorphic K-means algorithm based on our proposed toolkit for clustering and design the homomorphic silhouette coefficient as the evaluation index, thereby providing an informative cluster assessment for local users with limited resources. Then, we simulate the protocols of our proposed toolkit to explore the parameter sensitivity in terms of computational efficiency. Last, we report security analysis to prove the security of our toolkit without privacy leakage to unauthorized parties. Comprehensive experiments further demonstrate the efficiency and utility of our toolkit.

A Privacy-preserving Outsourcing Scheme for Zero-knowledge Proof Generation

A Privacy-preserving Outsourcing Scheme for Zero-knowledge Proof Generation

A novel privacy-preserving outsourcing computation scheme for Canny edge detection

A novel privacy-preserving outsourcing computation scheme for Canny edge detection

Lattice-based weak-key analysis on single-server outsourcing protocols of modular exponentiations and basic countermeasures

We investigate the problem of securely outsourcing the modular exponentiations in cryptography to an untrusted server, and analyze the security and the efficiency of three privacy-preserving outsourcing protocols for exponentiations proposed in Ding et al. (2017) [18]. Based on Coppersmith's lattice-based method, we present heuristic polynomial-time and ciphertext-only weak-key attacks on these protocols, which shows that the recommended size of the secret keys in their protocols can not assure the input privacy of the exponents. Correspondingly, we explicitly estimate the size of the secure secret keys to circumvent our attacks, and analyze the efficiency of the revised protocols with security settings. Our theoretical analysis and experimental results demonstrate that the protocol of single modular exponentiation is unavailable, the protocol of simultaneous modular exponentiations is not so efficient as claimed but still available, and the protocol of multiple modular exponentiations becomes more efficient as the number of exponentiations increases.