Secure Outsourcing Research Articles

Hierarchical Threshold Multi-Key Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) supports computation on encrypted data without the need for decryption, thereby enabling secure outsourcing of computing to an untrusted cloud. Subsequently, motivated by application scenarios where private information is offered by different data owners, Multi-Key Fully Homomorphic Encryption (MKFHE) and Threshold Fully Homomorphic Encryption (ThFHE) were successively introduced. However, both MKFHE and ThFHE have some limitations: MKFHE requires the participation of all members during the decryption process and does not support decryption using a subset of members, while ThFHE requires pre-fixed participants and does not support dynamic joining or exiting.To address these limitations, in this paper, we propose a new notion called Hierarchical Threshold Multi-key Fully Homomorphic Encryption (HTM-FHE), which combines the features of MKFHE and ThFHE, incorporating the advantages of both. Then we provide the first construction of HTM-FHE based on lattice, denoted as HTM-TFHE. Our scheme can evaluate a binary gate on ciphertexts encrypted under different groups’ public keys followed by a bootstrapping procedure. The semantic and simulation security of HTM-TFHE is proven under the LWE assumption. Furthermore, HTM-TFHE supports fine-grained access control for encrypted data, which provides benefits in practical applications.

Secure Outsourcing Evaluation for Sparse Decision Trees

Secure Outsourcing Evaluation for Sparse Decision Trees

A Review on Secure Outsourcing and Privacy-Preserving Traffic Monitoring in Fog-Enabled VANETs

New possibilities for fog-based vehicle monitoring have emerged with the expansion of fog computing, but present privacy concerns provide a significant barrier that limits the extent to which vehicles can participate. Because of its potential to improve road network security and vehicle productivity, the field of Vehicle-based Ad hoc Networks (VANETs) is gaining prominence. The security problems with VANETs, such as data confidentiality and message access control are still need better solutions to provide better Quality of Service (QoS). The effectiveness of VANET networks is diminished due to their instability problem. Vehicles continually add requests to the Road Side Unit (RSU) queue whenever they want specific information. For ever-evolving networks like VANETs, better routing is a continual process. Fundamental problems arise in large-scale systems when centralized procedures are used to assign jobs to the nodes along a route. The present centralized system for computing and safety has many needs, including the protection of data storage, user authentication, access control, system availability across multiple network connections, and the provision of a real-time data flow overview. Distributed problem solving and work sharing between multiple agents can improve the system's scalability. This paper provides a brief survey on the secured outsourcing and privacy preservation-based traffic monitoring model with routing and task scheduling models in Fog-enabled VANETS. This survey presents the limitations of the traditional models that help the researchers design new solutions for secure outsourcing in VANETs.

Regional Load Forecasting Scheme for Security Outsourcing Computation

Smart grids generate an immense volume of load data. When analyzed using intelligent technologies, these data can significantly improve power load management, optimize energy distribution, and support green energy conservation and emissions reduction goals. However, in the process of data utilization, a pertinent issue arises regarding potential privacy leakage concerning both regional and individual user power load data. This paper addresses the scenario of outsourcing computational tasks for regional power load forecasting in smart grids, proposing a regional-level load forecasting solution based on secure outsourcing computation. Initially, the scheme designs a secure outsourcing training protocol to carry out model training tasks while ensuring data security. This protocol guarantees that sensitive information, including but not limited to individual power consumption data, remains comprehensively safeguarded throughout the entirety of the training process, effectively mitigating any potential risks of privacy infringements. Subsequently, a secure outsourcing online prediction protocol is devised, enabling efficient execution of prediction tasks while safeguarding data privacy. This protocol ensures that predictions can be made without compromising the privacy of individual or regional power load data. Ultimately, experimental analysis demonstrates that the proposed scheme meets the requirements of privacy, accuracy, and timeliness for outsourcing computational tasks of load forecasting in smart grids.

Information security outsourcing strategies in the supply chain considering security externality

Information assets that complement each other capture the information security characteristics of supply chain firms. Supply chain firms usually outsource security services to Managed Security Service Providers (MSSPs) to protect their information. Considering the security externality and information leakage risk in security outsourcing, this article investigates four security management scenarios faced by two supply chain firms. We try to answer whether both firms should outsource security to the same or different MSSPs. We find that no matter under which scenario, as the complementation degree increases, both firms and MSSPs decrease the security quality, and MSSPs offer a contract with lower compensation. We show that information leakage risk and security externality have different effects on the MSSP’s optimal strategy selections. Besides, the MSSP tends to serve both firms when the security externality is positive but prefers serving only one firm when the security externality is negative. Moreover, we find that the strategy that one firm outsources to an MSSP and its partner manages it in-house can be the optimal selection for the social planner but not the MSSP’s optimal strategy. We also extend the model and find that the results are robust to the situations of uncertain loss and asymmetric loss.

The Outsourcing of State Security: A Case Study of US Intervention in Laos, 1962–1975

The United States (US) involvement in covert action abroad has received significant scholarly attention, including the outsourcing of force to third parties, such as foreign death squads, private paramilitaries, and more recently private military and security companies. However, less attention has been paid to the routine and everyday mechanisms the US state uses to administer outsourced force, and the impacts this has both on the combatants and civilians. Through a historical examination of the covert conflict in Laos from 1962–1975, this article documents the routine administrative mechanisms the US state employed to prosecute an illegal war. This article’s principal contribution to the state crime literature is the documentation of the hidden harms this generated both for the combatants used and the civilians affected by the violence. This contribution is grounded in interviews conducted with ex-service people from the conflict period and archival research, including recently declassified materials.

Enabling privacy-preserving non-interactive computation for Hamming distance

Hamming distance is a measure of the similarity between two strings of the same length. Privacy-preserving Hamming distance computation allows data users to obtain the Hamming distance between their data without disclosing their respective private data. The existing privacy-preserving protocols for Hamming distance computation require multiple rounds of online interactive computation between two data users. To address this issue, we propose a new Privacy-preserving non-interactive Hamming Distance Computation (PHDC) protocol. Different from previous works, we adopt the strategy of secure outsourcing to avoid the online interactive computation between data users. We move the Hamming distance computation from the user side to the cloud side. The cloud server is responsible for the Hamming distance computation under privacy protection. To preserve data privacy, we propose a novel blinding technique for user data. Data users initially blind their data using homomorphic encryption and randomization techniques. The edge server is responsible for data aggregation and further data blindness. In this way, data users only need to outsource their encrypted data to the edge server, and there is no online interactive computation between data users. With the assistance of the edge server and the cloud server, the privacy-preserving Hamming distance computation is achieved. The security analysis demonstrates that the protocol guarantees the data privacy under the semi-honest adversarial model. The theoretical analysis and experimental results illustrate the efficiency of the proposed protocol.

Secure and fine-grained access control of electronic health record using SAPCP-ABE technique in cloud

This study explores how Electronic Health Records (EHR) might be transformed in the context of the rapid improvements in cloud computing and IoT technology. But worries about sensitive data security and access management when it moves to large cloud provider networks surface. Even if they are secure, traditional encryption techniques sometimes lack the granularity needed for effective data protection. We suggest the Secure Access Policy – Ciphertext Policy – Attribute-based Encryption (SAPCP-ABE) algorithm as a solution to this problem. This method ensures that only authorized users may access the necessary data while facilitating fine-grained encrypted data exchange. The three main phases of SAPCP-ABE are retrieval and decoding, where the system verifies users’ access restrictions, secure outsourcing that prioritizes critical attributes, and an authenticity phase for early authentication. Performance tests show that SAPCP-ABE is a better scheme than earlier ones, with faster encryption and decryption speeds of 5 and 5.1 seconds for 512-bit keys, respectively. Security studies, numerical comparisons, and implementation outcomes demonstrate our suggested approach’s efficacy, efficiency, and scalability.

A Bitcoin-based Secure Outsourcing Scheme for Optimization Problem in Multimedia Internet of Things

With the development of the Internet of Things (IoT) and cloud computing, various multimedia data such as audio, video, and images have experienced explosive growth, ushering in the era of big data. Large-scale computing tasks in the Multimedia Internet of Things (M-IoT), such as mathematical optimization problems, have begun to be outsourced from IoT devices with limited computing power to cloud servers for execution. However, outsourcing computation brings security concerns, because the behaviors of clouds are invisible to users. The leakage of privacy data in outsourced optimization problems leads to immeasurable losses. The mutual distrust between clouds and users causes that the correctness of the optimal decisions and the fairness of the payment activities are not guaranteed. Blockchain technology has the characteristic of immutability and has become a new security paradigm for eliminating multi-party trust concerns. In this article, we propose a Bitcoin-based secure outsourcing scheme to address the aforementioned security concerns. To prevent confidential data leakage, the proposed scheme designs a computable privacy-preserving method for the outsourced optimization problems. To judge the correctness of the optimal decision and reduce verification costs, the proposed scheme designs a low-cost two-layer verification mechanism based on dual theory and blockchain technology. Blockchain nodes reach a consensus on the problem solutions and trigger an automatic fair payment protocol-based Bitcoin. Security analysis and experimental results demonstrate that our scheme guarantees privacy, fairness, and computational efficiency.

The Effect Of Service Quality And Marketing Mix On Customer Loyalty Outsourcing Security PT. Tri Garda Abbas East Jakarta

The purpose of this research is to determine the effect of service quality on customer loyalty marketing mix towards customer loyalty and know the quality of shared services marketing mix on customer loyalty Outsourcing security PT. Tri Garda Abbas, East Jakarta. This research uses a quantitative descriptive method with data collection techniques through distributing questionnaires and library data. This research uses the method of purposive sampling saturated used amount 97 employee PT. Tri Garda Abbas, East Jakarta, using multiple linear regression data analysis techniques. The results of this research show that there is an influence of service quality on customer loyalty marketing mix on customer loyalty, and there is an influence on service quality and marketing mix simultaneously or together on customer loyalty Outsourcing security PT. Tri Garda Abbas, East Jakarta, has an influential contribution of 33,5% on the customer loyalty variable. It is hoped that the results of this research will contribute to further research

On Efficient Parallel Secure Outsourcing of Modular Exponentiation to Cloud for IoT Applications

Modular exponentiation is crucial for secure data exchange in cryptography, especially for resource-constrained Internet of Things (IoT) devices. These devices often rely on third-party servers to handle computationally intensive tasks like modular exponentiation. However, existing outsourcing solutions for the RSA algorithm may have security vulnerabilities. This work identifies a critical flaw in a recent outsourcing protocol for RSA proposed by Hu et al. We demonstrate how this flaw compromises the security of the entire RSA system. Subsequently, we propose a robust solution that strengthens the RSA algorithm and mitigates the identified vulnerability. Furthermore, our solution remains resilient against existing lattice-based attacks. The proposed fix offers a more secure and efficient way for IoT devices to leverage the power of third-party servers while maintaining data integrity and confidentiality. An extensive performance evaluation confirms that our solution offers comparable efficiency while significantly enhancing security compared to existing approaches.

Encrypted Domain Secret Medical-Image Sharing With Secure Outsourcing Computation in IoT Environment

Encrypted Domain Secret Medical-Image Sharing With Secure Outsourcing Computation in IoT Environment

A Review on Technologies of Secure Outsourced Association Rule Mining in Cloud Computing Environment

A Review on Technologies of Secure Outsourced Association Rule Mining in Cloud Computing Environment

HEaaN-NB: Non-interactive Privacy-Preserving Naive Bayes using CKKS for Secure Outsourced Cloud Computing

HEaaN-NB: Non-interactive Privacy-Preserving Naive Bayes using CKKS for Secure Outsourced Cloud Computing

SOCI+: An Enhanced Toolkit for Secure Outsourced Computation on Integers

SOCI+: An Enhanced Toolkit for Secure Outsourced Computation on Integers

Controllable Privacy-Preserving Online Diagnosis with Outsourced SVM over Encrypted Medical Data

With the widespread application of online diagnosis systems, users can upload their physical characteristics anytime and from anywhere to receive clinical diagnoses. However, for privacy and intellectual property considerations, users' physical characteristics, diagnosis results, and the medical diagnosis model should be protected. To achieve an efficient and secure online diagnosis, secure outsourcing and low burden become research objectives. However, few of the existing privacy-preserving schemes focus on the secure outsourcing of the training process, and few consider the supervision of the hospital for the online diagnosis process. By introducing a four-party architecture with two non-colluding servers, a hospital and users, in this paper, we propose a controllable privacy-preserving online diagnosis scheme (CPPOD) with outsourced SVM over encrypted medical data. Concretely, an integer vector homomorphic encryption is employed to protect medical data and user requests. In the encrypted domain, a series of collaborative protocols including data collection, sequence minimum optimization solver, SVM model building, and online diagnosis are constructed and take place between different participants, while no significant increase in computation on either the hospital or user side. CPPOD enables the hospital to delegate online diagnosis services to a cloud server while ensuring that its regulatory capabilities cannot be bypassed unauthorized. Security analysis and performance evaluation suggest that CPPOD performs well regarding security and efficiency.

Verifiable Privacy-Enhanced Rotation Invariant LBP Feature Extraction in Fog Computing

Rotation invariant local binary pattern (RI-LBP) features have been applied in diverse scenarios with the advantages of gray-scale and rotation invariance. Secure fog computing has become an emerging paradigm for enterprises or individuals with a huge volume of private data but limited computing power for feature extraction. Prior secure outsourcing protocols based on LBP and RI-LBP simply focus on local data privacy, which can only resist ciphertext-only attack, and also make extracted features exposed to the cloud. This work focuses on how to effectively ensure data confidentiality and feature integrity. We propose a verifiable privacy-enhanced protocol for RI-LBP feature extraction (VRLBP) based on the fog computing paradigm, which mitigates the aforementioned challenges by involving the proposed symmetric cryptographic scheme where local data and extracted features are proven secure against chosen plaintext attack. Meanwhile, the stage of verification can check the correctness of outsourced features with an overwhelming probability and constant computational complexity. The security analysis and computational costs demonstrate that VRLBP can reduce the computation overhead to around 30% of original feature extraction in a privacy-preserving manner. To exhibit the practical utility, VRLBP is implemented for deepfake detection on five public datasets. Extensive evaluations indicate that VRLBP achieves almost the same accuracy as the original RI-LBP algorithm and outperforms the state-of-the-art protocols.

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.

Shielding Graph for eXact Analytics With SGX

Graphs nicely capture data from various domains, allowing the computations of many analytic tasks via graph queries. Graphs of real-world data are often large, albeit useful, and the involved computation can be too heavyweight for commodity computers. For secure outsourcing, we propose (SGX) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> , a forward-secure structured encryption scheme for graph data, which uses lightweight cryptographic techniques with a trusted execution environment such as SGX. To process million-scale graphs by the limited memory of SGX, we load data on-demand using Dijkstra's algorithm and Fibonacci heap. Compared with most prior graph encryption schemes, (SGX) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> supports exact shortest-distance queries instead of approximation and can be easily extended to other graph-based analytics. Finally, we discuss some generic enhancements addressing active adversaries trying to exploit leakages originating from SGX.

Secure IoT-enabled sharing of digital medical records: An integrated approach with reversible data hiding, symmetric cryptosystem, and IPFS

In the digitizing medical field, secure sharing of patients’ medical records over the Internet while preserving privacy is critical. Medical image encryption safeguards healthcare, IoMT, and cloud services from increasing digital threats. Incorporating vital diagnostic data like blood pressure, haemoglobin levels, sugar levels, body temperature, and medical images such as X-rays, CT, and MRI scans, comprehensive medical records benefit from Internet of Things (IoT) technology. This research paper presents a secure solution for sharing medical records among medical experts. The system employs the tamper-proof InterPlanetary File System (IPFS) for secure storage and distribution. It establishes a cryptographic association between medical reports and images, enabling secure outsourcing and retrieval by authorized users. This is achieved through the use of the reversible data hiding technique based on the Parametric Binary Tree Labeling (PBTL) scheme, efficiently linking Medical Reports and Images. To ensure robust security, the Medical Report in PDF format is encrypted using the Advanced Encryption Standard (AES-256) for symmetric encryption, and the encrypted file is securely uploaded to IPFS, generating a hash value serving as a reliable reference for future use. Furthermore, IPFS treats the hash value as confidential information and embeds it into the medical image using a novel Algorithm to embed secrets. Subsequently, the embedded image undergoes encryption before being uploaded to IPFS. The extensive evaluation and comparison with existing systems affirm the effectiveness of the proposed method in ensuring security and privacy preservation. Various analyses, including image quality, sensitivity, security, correlation, quality, and attack robustness, demonstrate the efficiency and strength of the proposed approach.