Privacy preserving machine learning is an active area of research usually relying on techniques such as homomorphic encryption or secure multiparty computation. Recent novel encryption techniques for performing machine learning using deep neural nets on images have recently been proposed by Tanaka and Sirichotedumrong, Kinoshita, and Kiya. We present new chosen-plaintext and ciphertext-only attacks against both of these proposed image encryption schemes and demonstrate the attacks' effectiveness on several examples.
In recent years, privacy-preserving methods for deep learning have become an urgent problem. Accordingly, we propose the combined use of federated learning (FL) and encrypted images for privacy-preserving image classification under the use of the vision transformer (ViT). The proposed method allows us not only to train models over multiple participants without directly sharing their raw data but to also protect the privacy of test (query) images for the first time. In addition, it can also maintain the same accuracy as normally trained models. In an experiment, the proposed method was demonstr…
Georgia State University（佐治亚州立大学）；Rochester Institute of Technology（罗切斯特理工学院）
Outsourced computation for neural networks allows users access to state of the art models without needing to invest in specialized hardware and know-how. The problem is that the users lose control over potentially privacy sensitive data. With homomorphic encryption (HE) computation can be performed on encrypted data without revealing its content. In this systematization of knowledge, we take an in-depth look at approaches that combine neural networks with HE for privacy preservation. We categorize the changes to neural network models and architectures to make them computable over HE and how th…
The University of Tokyo（东京大学）；Michigan State University（密歇根州立大学）；Carnegie Mellon University（卡内基梅隆大学）
We propose a privacy-preserving framework for learning visual classifiers by leveraging distributed private image data. This framework is designed to aggregate multiple classifiers updated locally using private data and to ensure that no private information about the data is exposed during and after its learning procedure. We utilize a homomorphic cryptosystem that can aggregate the local classifiers while they are encrypted and thus kept secret. To overcome the high computational cost of homomorphic encryption of high-dimensional classifiers, we (1) impose sparsity constraints on local classi…
Institute for Development and Research in Banking Technology（银行技术发展与研究所）；University of Hyderabad（海得拉巴大学）
The Machine Learning and Deep Learning Models require a lot of data for the training process, and in some scenarios, there might be some sensitive data, such as customer information involved, which the organizations might be hesitant to outsource for model building. Some of the privacy-preserving techniques such as Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation can be integrated with different Machine Learning and Deep Learning algorithms to provide security to the data as well as the model. In this paper, we propose a Chaotic Extreme Learning Machine and its …
Privacy-preserving deep neural networks (DNNs) have been proposed for protecting data privacy in the cloud server. Although several encryption schemes for visually protection have been proposed for privacy-preserving DNNs, several attacks enable to restore visual information from encrypted images. On the other hand, it has been confirmed that the block-wise image encryption scheme which utilizes block and pixel shuffling is robust against several attacks. In this paper, we propose a jigsaw puzzle solver-based attack to restore visual information from encrypted images including block and pixel …
University of Stavanger（斯塔万格大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）；Old Dominion University（奥尔多明尼昂大学）；Norwegian University of Science and Technology（挪威科技大学）
Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applica…
Secure cloud storage is an issue of paramount importance that both businesses and end-users should take into consideration before moving their data to, potentially, untrusted clouds. Migrating data to the cloud raises multiple privacy issues, as they are completely controlled by a cloud provider. Hence, an untrusted cloud provider can potentially breach users; privacy and gain access to sensitive information. The problem becomes even more pronounced when the could provider is required to store a statistical database and periodically publish analytics. In this work, we first present a detailed …
In this paper, we propose a novel generative model-based attack on learnable image encryption methods proposed for privacy-preserving deep learning. Various learnable encryption methods have been studied to protect the sensitive visual information of plain images, and some of them have been investigated to be robust enough against all existing attacks. However, previous attacks on image encryption focus only on traditional cryptanalytic attacks or reverse translation models, so these attacks cannot recover any visual information if a block-scrambling encryption step, which effectively destroys…
Recent trend towards cloud computing paradigm, smart devices and 4G wireless technologies has enabled seamless data sharing among users. Cloud computing environment is distributed and untrusted, hence data owners have to encrypt their data to enforce data confidentiality. The data confidentiality in a distributed environment can be achieved by using attribute-based encryption technique. Decentralized attribute-based encryption technique is a variant of multiple authority based attribute-based encryption whereby any attribute authority can independently join and leave the system without collabo…
Recently, Government of India has taken several initiatives to make India digitally strong such as to provide each resident a unique digital identity, referred to as Aadhaar, and to provide several online e-Governance services based on Aadhaar such as DigiLocker. DigiLocker is an online service which provides a shareable private storage space on public cloud to its subscribers. Although DigiLocker ensures traditional security such as data integrity and secure data access, privacy of e-documents are yet to addressed. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) can improve data privacy…
This paper aims to evaluate the safety of a pixel-based image encryption method, which has been proposed to apply images with no visual information to deep neural networks (DNN), in terms of robustness against ciphertext-only attacks (COA). In addition, we propose a novel DNN-based COA that aims to reconstruct the visual information of encrypted images. The effectiveness of the proposed attack is evaluated under two encryption key conditions: same encryption key, and different encryption keys. The results show that the proposed attack can recover the visual information of the encrypted images …
Privacy in form of anonymous communication could be comparably both faster and harder to break in optical routers than in today's anonymous IP networks based on The Onion Routing (Tor). Implementing the practical privacy alloptically,however, is not straightforward, as it requires key generation in each anonymization node to avoid distribution of long keys, and layered encryption, both at the optical line rate. Due to the unavailability of cryptographically strong optical key generation and encryption components, not only a layered encryption is a challenge, but an optical encryption in genera…
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning. While these innovative and ground-breaking applications can be considered as a boon, at the same time they raise significant privacy concerns. In fact, recent GDPR (General Data Protection Regulation) legislation has highlighted and become an incentive for privacy-preserving solutions. Typical privacy-preserving video monitoring schemes address these concerns by either anonymizing the sensitive data. However, these approaches suffer …
Searchable symmetric encryption (SSE) enables a client to perform searches over its outsourced encrypted files while preserving privacy of the files and queries. Dynamic schemes, where files can be added or removed, leak more information than static schemes. For dynamic schemes, forward privacy requires that a newly added file cannot be linked to previous searches. We present a new dynamic SSE scheme that achieves forward privacy by replacing the keys revealed to the server on each search. Our scheme is efficient and parallelizable and outperforms the best previous schemes providing forward pr…
Recently, Lu et al. have proposed two image search schemes based on additive homomorphic encryption [IEEE Access, 2 (2014), 125-141]. We remark that both two schemes are flawed because: (1) the first scheme does not make use of the additive homomorphic property at all; (2) the additive homomorphic encryption in the second scheme is unnecessary and can be replaced by a more efficient symmetric key encryption.
FL-DABE-BC: A Privacy-Enhanced, Decentralized Authentication, and Secure Communication for Federated Learning Framework with Decentralized Attribute-Based Encryption and Blockchain for IoT Scenarios
Recently, deep learning as a service (DLaaS) has emerged as a promising way to facilitate the employment of deep neural networks (DNNs) for various purposes. However, using DLaaS also causes potential privacy leakage from both clients and cloud servers. This privacy issue has fueled the research interests on the privacy-preserving inference of DNN models in the cloud service. In this paper, we present a practical solution named BAYHENN for secure DNN inference. It can protect both the client's privacy and server's privacy at the same time. The key strategy of our solution is to combine homomor…
Privacy has gained a growing interest nowadays due to the increasing and unmanageable amount of produced confidential data. Concerns about the possibility of sharing data with third parties, to gain fruitful insights, beset enterprise environments; value not only resides in data but also in the intellectual property of algorithms and models that offer analysis results. This impasse locks both the availability of high-performance computing resources in the "as-a-service" paradigm and the exchange of knowledge with the scientific community in a collaborative view. Privacy-preserving data science…
Dynamic searchable symmetric encryption (DSSE) is a useful cryptographic tool in encrypted cloud storage. However, it has been reported that DSSE usually suffers from file-injection attacks and content leak of deleted documents. To mitigate these attacks, forward privacy and backward privacy have been proposed. Nevertheless, the existing forward/backward-private DSSE schemes can only support single keyword queries. To address this problem, in this paper, we propose two DSSE schemes supporting range queries. One is forward-private and supports a large number of documents. The other can achieve …
Online Social Networks (OSNs) have become one of the most important activities on the Internet, such as Facebook and Google+. However, security and privacy have become major concerns in existing C/S based OSNs. In this paper, we propose a novel scheme called a Privacy-preserving Community-based P2P OSNs Using Broadcast Encryption Supporting Recommendation Mechanism (PCBE) that supports cross-platform availability in stringent privacy requirements. For the first time, we introduce recommendation mechanism into a privacy-preserving P2P based OSNs, in which we firstly employ the Open Directory Pr…
In this paper, we propose a novel learnable image encryption method for privacy-preserving deep neural networks (DNNs). The proposed method is carried out on the basis of block scrambling used in combination with data augmentation techniques such as random cropping, horizontal flip and grid mask. The use of block scrambling enhances robustness against various attacks, and in contrast, the combination with data augmentation enables us to maintain a high classification accuracy even when using encrypted images. In an image classification experiment, the proposed method is demonstrated to be effe…
SecureDrive-FL: Joint Differential Privacy and Gradient-Aware Selective Homomorphic Encryption for Federated Driver Monitoring
Institute of Industrial Automation and Software Engineering, University of Stuttgart（斯图加特大学工业自动化与软件工程研究所）；Eindhoven University of Technology (TU/e)（埃因霍温理工大学）
Owing to a number of reasons, the deployment of encryption solutions are beginning to be ubiquitous at both organizational and individual levels. The most emphasized reason is the necessity to ensure confidentiality of privileged information. Unfortunately, it is also popular as cyber-criminals' escape route from the grasp of digital forensic investigations. The direct encryption of data or indirect encryption of storage devices, more often than not, prevents access to such information contained therein. This consequently leaves the forensics investigation team, and subsequently the prosecutio…
The security of learnable image encryption schemes for image classification using deep neural networks against several attacks has been discussed. On the other hand, block scrambling image encryption using the vision transformer has been proposed, which applies to lossless compression methods such as JPEG standard by dividing an image into permuted blocks. Although robustness of the block scrambling image encryption against jigsaw puzzle solver attacks that utilize a correlation among the blocks has been evaluated under the condition of a large number of encrypted blocks, the security of encry…
Biometric matching involves storing and processing sensitive user information. Maintaining the privacy of this data is thus a major challenge, and homomorphic encryption offers a possible solution. We propose a privacy-preserving biometrics-based authentication protocol based on fully homomorphic encryption, where the biometric sample for a user is gathered by a local device but matched against a biometric template by a remote server operating solely on encrypted data. The design ensures that 1) the user's sensitive biometric data remains private, and 2) the user and client device are securely…
University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）
Honey Encryption is an approach to encrypt the messages using low min-entropy keys, such as weak passwords, OTPs, PINs, credit card numbers. The ciphertext is produces, when decrypted with any number of incorrect keys, produces plausible-looking but bogus plaintext called "honey messages". But the current techniques used in producing the decoy plaintexts do not model human language entirely. A gibberish, random assortment of words is not enough to fool an attacker; that will not be acceptable and convincing, whether or not the attacker knows some information of the genuine source. In this pape…
As an effective resource allocation approach, double auctions (DAs) have been extensively studied in electronic commerce. Most previous studies have focused on how to design strategy-proof DA mechanisms, while not much research effort has been done concerning privacy and security issues. However, security, especially privacy issues have become such a public concern that the European governments lay down the law to enforce the privacy guarantees recently. In this paper, to address the privacy issue in electronic auctions, we concentrate on how to design a privacy-preserving mechanism for double…
University of Victoria（维多利亚大学）；Helwan University（赫勒万大学）；National Research Council of Canada（加拿大国家研究委员会）
Precision medicine is an emerging approach for disease treatment and prevention that delivers personalized care to individual patients by considering their genetic makeups, medical histories, environments, and lifestyles. Despite the rapid advancement of precision medicine and its considerable promise, several underlying technological challenges remain unsolved. One such challenge of great importance is the security and privacy of precision health-related data, such as genomic data and electronic health records, which stifle collaboration and hamper the full potential of machine-learning (ML) …
Ciphertexts of an order-preserving encryption (OPE) scheme preserve the order of their corresponding plaintexts. However, OPEs are vulnerable to inference attacks that exploit this preserved order. At another end, differential privacy has become the de-facto standard for achieving data privacy. One of the most attractive properties of DP is that any post-processing (inferential) computation performed on the noisy output of a DP algorithm does not degrade its privacy guarantee. In this paper, we propose a novel differentially private order preserving encryption scheme, OP$ε$. Under OP$ε$, the l…
Entity resolution is the task of disambiguating records that refer to the same entity in the real world. In this work, we explore adapting one of the most efficient and accurate Jaccard-based entity resolution algorithms - PPJoin, to the private domain via homomorphic encryption. Towards this, we present our precise adaptation of PPJoin (HE-PPJoin) that details certain subtle data structure modifications and algorithmic additions needed for correctness and privacy. We implement HE-PPJoin by extending the PALISADE homomorphic encryption library and evaluate over it for accuracy and incurred ove…
We present a novel privacy-preserving scheme for deep neural networks (DNNs) that enables us not to only apply images without visual information to DNNs for both training and testing but to also consider data augmentation in the encrypted domain for the first time. In this paper, a novel pixel-based image encryption method is first proposed for privacy-preserving DNNs. In addition, a novel adaptation network is considered that reduces the influence of image encryption. In an experiment, the proposed method is applied to a well-known network, ResNet-18, for image classification. The experimenta…
Increasing incidents of security compromises and privacy leakage have raised serious privacy concerns related to cyberspace. Such privacy concerns have been instrumental in the creation of several regulations and acts to restrict the availability and use of privacy-sensitive data. The secure computation problem, initially and formally introduced as secure two-party computation by Andrew Yao in 1986, has been the focus of intense research in academia because of its fundamental role in building many of the existing privacy-preserving approaches. Most of the existing secure computation solutions …
PRISE: Privacy-pReserving Image Searchable Encryption Scheme for Intelligent Vehicle Systems
Hainan University（海南大学）；Zhengzhou University（郑州大学）；St. Francis Xavier University（圣弗朗西斯泽维尔大学）；Putian University（莆田学院）；Universiti Putra Malaysia（马来西亚博特拉大学）
Federated learning (FL) has emerged as a privacy solution for collaborative distributed learning where clients train AI models directly on their devices instead of sharing their data with a centralized (potentially adversarial) server. Although FL preserves local data privacy to some extent, it has been shown that information about clients' data can still be inferred from model updates. In recent years, various privacy-preserving schemes have been developed to address this privacy leakage. However, they often provide privacy at the expense of model performance or system efficiency, and balanci…
The privacy aspect of state estimation algorithms has been drawing high research attention due to the necessity for a trustworthy private environment in cyber-physical systems. These systems usually engage cloud-computing platforms to aggregate essential information from spatially distributed nodes and produce desired estimates. The exchange of sensitive data among semi-honest parties raises privacy concerns, especially when there are coalitions between parties. We propose two privacy-preserving protocols using Kalman filter and partially homomorphic encryption of the measurements and estimate…
