Privacy-Preserving Outsource Computing for Binary Vector Similarity

Biometric identification systems offer strong security by relying on unique personal traits. At the same time, they raise significant privacy concerns because compromised biometric data cannot be revoked. This paper explores the use of homomorphic encryption (HE) as a means to protect biometric data during classification and reduce the risk of exposing sensitive information. Our system comprises a feature extractor which operates locally and a classifier which processes encrypted data. We demonstrate the feasibility of our approach on a gait recognition task, employing a vision transformer as a feature extractor and training several HE-compatible classifiers. Through a comprehensive statistical analysis, we evaluate the impact of HE on accuracy and computational complexity, especially with different activation functions and their polynomial approximations. Our results demonstrate the feasibility of secure and accurate gait recognition using HE, while highlighting the trade-off between security and performance.

Machine learning servers with mass storage and computing power is an ideal platform to store, manage, and analyze data and support decision-making. However, the main issue is providing security and privacy to the data, as the data is stored in a public way. Recently, homomorphic data encryption has been proposed as a solution due to its capabilities in performing computations over encrypted data. In this paper, we proposed an encrypted all convolutional net that transformed traditional all convolutional net into a net based on homomorphic encryption. This scheme allows different data holders to send their encrypted data to cloud service, complete predictions, and return them in encrypted form as the cloud service provider does not have a secret key. Therefore, the cloud service provider and others cannot get unencrypted raw data. When applied to the MNIST database, privacy-preserving all convolutional based on homomorphic encryption predict efficiently, accurately and with privacy protection.

The protection and processing of sensitive data in big data systems are common problems as the increase in data size increases the need for high processing power. Protection of the sensitive data on a system that contains multiple connections with different privacy policies, also brings the need to use proper cryptographic key exchange methods for each party, as extra work. Homomorphic encryption methods can perform similar arithmetic operations on encrypted data in the same way as a plain format of the data. Thus, these methods provide data privacy, as data are processed in the encrypted domain, without the need for a plain form and this allows outsourcing of the computations to cloud systems. This also brings simplicity on key exchange sessions for all sides. In this paper, we propose novel privacy preserving clustering methods, alongside homomorphic encryption schemes that can run on a common high performance computation platform, such as a cloud system. As a result, the parties of this system will not need to possess high processing power because the most power demanding tasks would be done on any cloud system provider. Our system offers a privacy preserving distance matrix calculation for several clustering algorithms. Considering both encrypted and plain forms of the same data for different key and data lengths, our privacy preserving training method’s performance results are obtained for four different data clustering algorithms, while considering six different evaluation metrics.

Smart furniture is gradually entering people's lives. The special nature of smart furniture determines that it will be exposed to a large amount of private information, but the data processing behind smart furniture still lacks sufficient security. Therefore, the research topic of this paper is the possibility of using homomorphic encryption in smart furniture, and the research topic of this paper is a smart furniture data security solution based on homomorphic encryption. The research methodology of this paper is as follows: investigating the solutions and problems of mainstream smart furniture manufacturers in terms of data security and the possibility of enhancing protection with homomorphic encryption, as well as the impact on cost issues and applicability. The study shows that the use of homomorphic encryption improves the security of remote control and data transmission of smart furniture using cloud-based technologies. In addition, it increases the versatility and reduces the cost of smart furniture and improves the compatibility between smart furniture.

Abstract: Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.

Cloud Service Providers, exemplified by industry leaders like Google Cloud Platform, Microsoft Azure, and Amazon Web Services, deliver a dynamic array of cloud services in an ever-evolving landscape. This sector is witnessing substantial growth, with enterprises such as Netflix and PayPal heavily relying on cloud infrastructure for various needs such as data storage, computational resources, and various other services. The adoption of cloud solutions by businesses not only facilitates cost reduction but also fosters flexibility and supports scalability. Despite the undeniable advantages, concerns surrounding security and privacy persist in the realm of Cloud Computing. Given that Cloud services are accessible via the internet, there is a potential vulnerability to unauthorized access by hackers or malicious entities from anywhere in the world. A crucial aspect of addressing this challenge is the implementation of robust security measures, particularly focusing on data protection. To safeguard data in the Cloud, a fundamental recommendation is the encryption of data prior to uploading. Encryption should be maintained consistently, both during storage and in transit. While encryption enhances security, it introduces a potential challenge for data owners who may need to perform various operations on their encrypted data, such as accessing, modifying, updating, deleting, reading, searching, or sharing them with others. One viable solution to balance the need for data security and operational functionality is the adoption of Searchable Encryption (SE). SE operates on encrypted data, allowing authorized users to perform certain operations without compromising the security of sensitive information. The effectiveness of SE has notably advanced since its inception, and ongoing research endeavors aim to further enhance its capabilities. This paper provides a comprehensive review of the functionality of Searchable Encryption, with a primary focus on its applications in Cloud services during the period spanning 2019 to 2023. Additionally, the study evaluates one of its prominent schemes, namely Fully Homomorphic Encryption (FHE). The analysis indicates an overall positive trajectory in SE research, showcasing increased efficiency as multiple functionalities are aggregated and rigorously tested.

e18725 Background: Healthcare data sharing is important for the creation of diverse and large data sets, supporting clinical decision making, and accelerating efficient research to improve patient outcomes. This is especially vital in the case of real world data analysis. However, stakeholders are reluctant to share their data without ensuring patients’ privacy, proper protection of their data sets and the ways they are being used. Homomorphic encryption is a cryptographic capability that can address these issues by enabling computation on encrypted data without ever decrypting it, so the analytics results are obtained without revealing the raw data. The aim of this study is to prove the accuracy of analytics results and the practical efficiency of the technology. Methods: A real-world data set of colorectal cancer patients’ survival data, following two different treatment interventions, including 623 patients and 24 variables, amounting to 14,952 items of data, was encrypted using leveled homomorphic encryption implemented in the PALISADE software library. Statistical analysis of key oncological endpoints was blindly performed on both the raw data and the homomorphically-encrypted data using descriptive statistics and survival analysis with Kaplan-Meier curves. Results were then compared with an accuracy goal of two decimals. Results: The difference between the raw data and the homomorphically encrypted data results, regarding all variables analyzed was within the pre-determined accuracy range goal, as well as the practical efficiency of the encrypted computation measured by run time, are presented in table. Conclusions: This study demonstrates that data encrypted with Homomorphic Encryption can be statistical analyzed with a precision of at least two decimal places, allowing safe clinical conclusions drawing while preserving patients’ privacy and protecting data owners’ data assets. Homomorphic encryption allows performing efficient computation on encrypted data non-interactively and without requiring decryption during computation time. Utilizing the technology will empower large-scale cross-institution and cross- stakeholder collaboration, allowing safe international collaborations. Clinical trial information: 0048-19-TLV. [Table: see text]

In this paper, privacy preserving at the cloud side is enabled by adopting Homomorphic Encryption (HE) as a major solution for protecting users' sensitive data. As a Proof Of Concept (POC), we take the case of a university that is adopting the cloud as a practical method for storing and operating over its private data. The main contribution of this application is that different data are stored encrypted at the cloud side. Students' data such as name, gender, date of birth, etc are encrypted using AES and hash functions. Students' indexes and grades are encrypted homomorphically using our modified Domingo Ferrer (DF) encryption scheme [1]. The main importance of the usage of HE in this application is allowing the process over encrypted data at the cloud side such as computing encrypted students' average. Different queries are sent from the university side to the cloud side, after operating and processing over encrypted data all results are shipped back encrypted to the university where the primitive data is recovered. Cloud infrastructure is created using Apache CloudStack and different encryption schemes are implemented under Python using SageMath library.

Secure computation, a methodology of computing on encrypted data, has become a key factor in machine learning. Homomorphic encryption (HE) enables computation on encrypted data without leaking any information to untrusted servers. In machine learning, the model selection method is a crucial algorithm that determines the performance and reduces the fitting problem. Despite the importance of finding the optimal model, none of the previous studies have considered model selection when performing data analysis through the HE scheme. The HE-based model selection we proposed finds the optimal complexity that best describes given data that is encrypted and whose distribution is unknown. Since this process requires a matrix calculation, we constructed the matrix multiplication and inverse of the matrix based on the bitwise operation. Based on these, we designed the model selection of the HE cross-validation approach and the HE Bayesian approach for homomorphic machine learning. Our focus was on evidence approximation for linear models to find goodness-of-fit that maximizes the evidence. We conducted an experiment on a dataset of age and Body Mass Index (BMI) from Kaggle to compare the capabilities and our model showed that encrypted data can regress homomorphically without decrypting it.

As globalization takes place, multiple data resources need to be integrated, and the degree of information flow between regions determines the degree of integration development between regions. Data sharing is even more sensitive when it comes to national and individual interests and is a matter of national security and national interest. Although Privacy-Preserving Computing achieves privacy protection for input data in the process of multi-party collaborative computing. However, the original data, computing process, and results face verification problems. As a result, sharing data on the blockchain will leak private information. To ensure the security of personal privacy information and recording in monitoring systems, This paper studies the use of homomorphic encryption for the secure transfer of blockchain data. Using our solution, the problem of data leakage in data sharing can be addressed by assuming that the data cannot be tampered with. This method has high transmission accuracy and a short transmission time, which effectively prevents data tampering caused by long transmission times.

This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication. The framework uses a fusion of electrocardiogram (ECG) and photoplethysmogram (PPG) signals when performing authentication. In addition to relying on the unique identification characteristics of the users’ biometric traits, the security of the framework is empowered by the use of Homomorphic Encryption (HE). The use of HE allows patients’ data to stay encrypted when being processed or analyzed in the cloud. Thus, providing not only a fast and reliable authentication mechanism, but also closing the door to many traditional security attacks. The framework’s performance was evaluated and validated using a machine learning (ML) model that tested the framework using a dataset of 25 users in seating positions. Compared to using just ECG or PPG signals, the results of using the proposed fused-based biometric framework showed that it was successful in identifying and authenticating all 25 users with 100% accuracy. Hence, offering some significant improvements to the overall security and privacy of personalized healthcare systems.

Any privacy leakage of biometric data poses severe security risks given their sensitive nature. Biometric templates should thus be protected, storing irreversibly transformed or encrypted biometric signals, while preserving the unprotected system's performance. Based on the recent developments by Zhu et al . on privacy preserving similarity evaluation of time series data, we present a new biometric template protection scheme based on homomorphic probabilistic encryption, where only encrypted data is stored or exchanged. We then apply the proposed scheme to signature verification and show that all requirements described in the ISO/IEC 24745 standard are met with no performance degradation, using a publicly available database and a free implementation of the Paillier cryptosystem. Moreover, the proposed approach is robust to hill-climbing attacks.

Statistical confidentiality focuses on protecting data to preserve its analytical value while preventing identity exposure, ensuring privacy and security in any system handling sensitive information. Homomorphic encryption allows computations on encrypted data without revealing it to anyone other than an owner or an authorized collector. When combined with other techniques, homomorphic encryption offers an ideal solution for ensuring statistical confidentiality. TFHE (Fast Fully Homomorphic Encryption over the Torus) is a fully homomorphic encryption scheme that supports efficient homomorphic operations on Booleans and integers. Building on TFHE, Zama’s Concrete project offers an open-source compiler that translates high-level Python code (version 3.9 or higher) into secure homomorphic computations. This study examines the feasibility of the Concrete compiler to perform core statistical analyses on encrypted data. We implement traditional algorithms for core statistical measures including the mean, variance, and five-point summary on encrypted datasets. Additionally, we develop a bitonic sort implementation to support the five-point summary. All implementations are executed within the Concrete framework, leveraging its built-in optimizations. Their performance is systematically evaluated by measuring circuit complexity, programmable bootstrapping count (PBS), compilation time, and execution time. We compare these results to findings from previous studies wherever possible. The results show that the complexity of sorting and statistical computations on encrypted data with the Concrete implementation of TFHE increases rapidly, and the size and range of data that can be accommodated is small for most applications. Nevertheless, this work reinforces the theoretical promise of Fully Homomorphic Encryption (FHE) for statistical analysis and highlights a clear path forward: the development of optimized, FHE-compatible algorithms.

Data security and privacy is a major concern for the users while using software services on the cloud. When users want to compute on a cloud service, traditional encryption schemes can be applied to encrypt and transfer the data to the cloud service. However, the service provider must decrypt the data for input into their computational model and thus the data content is exposed. If users do not want service providers to know what they are computing, then computing on encrypted data preserving privacy is an important issue. Homomorphic encryption is an encryption method where computations can be performed on the ciphertext, and the decrypted result of these computations is the same as if the computations were performed on the plaintext. However, the performance of this approach is currently inefficient. This paper presents an application of homomorphic encryption method on an open financial cloud framework (FinancialCloud) to perform calculations on encrypted data, therefore securing the data throughout the whole process. We demonstrate by example, showing that by applying improved algorithms can lessen the deficiencies induced by homomorphic encryptions.

An effective comparison protocol over encrypted data in cloud computing