Secure Decentralized Image Classification With Multiparty Homomorphic Encryption

This study addresses privacy protection challenges in federated learning by establishing a six-dimensional comparative framework encompassing "dataset type, accuracy loss, computational overhead, communication cost, privacy strength, and application domain." It systematically reviews representative works on differential privacy (DP), homomorphic encryption (HE), secure multi-party computation (SMPC), and their engineering variants MPHE-1, MHESA, and QSFL. Establishing a unified evaluation metric, we conclude: DP incurs minimal communication overhead but sacrifices accuracy; HE achieves near-cleartext accuracy with substantial computational overhead; SMPC offers strong security but is constrained by interaction rounds and network conditions; the three variants respectively address low-round scenarios, dropout scenarios, and edge efficiency scenarios. This paper provides contextualized selection recommendations and a reproducibility checklist, while proposing future research directions for large models and real-world network environments. It offers methodological and engineering references for compliant and implementable privacy-preserving federated learning.

Machine learning as a service (MLaaS) enables scalable model deployment and inference on cloud servers. However, MLaaS exposes user queries and model parameters to servers. To guarantee confidentiality of queries and model parameters, multi-party computation (MPC) enables secure inference by distributing data and computations across multiple service providers. MPC eliminates single points of failure, mitigates provider breaches and ensures confidentiality beyond legal agreements. Beyond confidentiality of queries and parameters, the model itself can memorize and leak training data during inference. To mitigate privacy concerns, differential privacy (DP) provides a formal privacy guarantee for training data, which can be satisfied by injecting carefully calibrated noise into gradients during training. However, naive combinations of DP and MPC amplify accuracy loss due to DP noise and MPC approximations, and incur high computational and communication overhead due to cryptographic operations. We present SPRINT, the first scalable solution for efficient MPC inference on DP fine-tuned models with high accuracy. SPRINT fine-tunes public pre-trained models on private data using DP. It integrates DP-specific optimizations, e.g., parameter-efficient fine-tuning and noise-aware optimizers, with MPC optimizations, e.g., cleartext public parameters and efficient approximations of non-linear functions. We evaluate SPRINT on GLUE benchmark with RoBERTa, achieving up to 1.6x faster MPC inference than the state-of-the-art non-DP solution SHAFT, reducing communication by 1.6x. Notably, SPRINT maintains high accuracy during MPC inference, with <1 percentage point gap compared to cleartext accuracy.

Secure multiparty computation protocol based on homomorphic encryption and its application in blockchain

In vehicular networks, drones can enhance performance by enabling real-time data collection, traffic monitoring, and communication optimization. However, due to the large, open, and highly dynamic nature of vehicular networks, ensuring security and achieving lightweight privacy protection are pressing challenges. Additionally, in traditional solutions, the control center (CC) often performs many tasks, leading to excessive computation and communication overhead, creating a system bottleneck. To address these issues, this paper proposes a privacy-preserving authentication scheme for FL in drone-assisted vehicular networks, where model parameter collection and model training are performed by drones in a privacy-preserving manner, significantly reducing the computation and communication overhead on the CC side. Moreover, the proposed scheme ensures strong privacy protection and efficient authentication by using federated learning, Elliptic Curve Cryptography (ECC), and the Chinese Remainder Theorem (CRT). The performance evaluation results show that the proposed scheme achieves a 17.3% reduction in computation overhead and a 22.8% reduction in communication overhead compared to existing solutions, highlighting its significant advantages.

Federated Learning (FL) has transformed AI applications in healthcare by enabling collaborative model training across multiple institutions while preserving patient data privacy. Despite its advantages, FL remains susceptible to security vulnerabilities, including model inversion attacks, adversarial data poisoning, and communication inefficiencies, necessitating enhanced privacy-preserving mechanisms. In response, this study introduces Privacy-Preserving Federated Learning (PPFL), an advanced FL framework integrating Secure Multi-Party Computation (SMPC), Differential Privacy (DP), and Homomorphic Encryption (HE) to ensure data confidentiality while maintaining computational efficiency. I rigorously evaluate PPFL using Federated Averaging (FedAvg), Secure Aggregation (SecAgg), and Differentially Private Stochastic Gradient Descent (DP-SGD) across real-world healthcare datasets. The results indicate that this approach achieves up to an 85% reduction in model inversion attack success rates, enhances privacy efficiency by 30%, and maintains accuracy retention between 95.2% and 98.3%, significantly improving security without compromising model performance. Furthermore, comparative visual analyses illustrate trade-offs between privacy and accuracy, scalability trends, and computational overhead. This study also explores scalability challenges, computational trade-offs, and real-world deployment considerations in multi-institutional hospital networks, paving the way for secure, scalable, and privacy-preserving AI adoption in healthcare environments.

Medical studies are usually time consuming, cumbersome and extremely costly to perform, and for exploratory research, their results are also difficult to predict a priori. This is particularly the case for rare diseases, for which finding enough patients is difficult and usually requires an international-scale research. In this case, the process can be even more difficult due to the heterogeneity of data-protection regulations, making the data sharing process particularly hard. In this short paper, we propose MedCo2 (pronounced MedCo square), a distributed system that streamlines the process of a medical study by bridging and enabling both data discovery and data analysis among multiple databases, while protecting data confidentiality and patients' privacy. MedCo2 relies on interactive protocols, homomorphic encryption and differential privacy. It enables the privacy-preserving computations of multiple statistics such as cosine similarity and variance, and the training of machine learning models, on patients that are obliviously selected according to specific criteria among multiple databases.

The exponential growth of data in recent years has created unprecedented opportunities for insights through big data analytics. However, this surge in data generation also raises significant privacy concerns, especially when sensitive information is involved. Privacy-preserving techniques have emerged as a critical area of research to ensure the ethical use of data while maintaining analytical efficacy. This paper explores state-of-the-art approaches to safeguarding privacy in big data analytics. Techniques such as data anonymization, differential privacy, secure multi-party computation, and homomorphic encryption are examined for their effectiveness and applicability across various domains. Anonymization techniques aim to remove personally identifiable information while retaining analytical utility, though challenges like re-identification attacks persist. Differential privacy introduces calibrated noise to data, balancing privacy and accuracy. Secure multi-party computation enables collaborative analytics without sharing raw data, promoting confidentiality in distributed environments. Homomorphic encryption allows computations on encrypted data, ensuring security throughout the analytical process. The integration of privacy-preserving techniques into big data workflows demands addressing computational overheads, scalability, and regulatory compliance. This paper also highlights how combining multiple privacy-preserving methods can mitigate individual limitations and enhance overall robustness. As privacy regulations evolve, such as the General Data Protection Regulation (GDPR) and similar frameworks, these techniques become increasingly vital.

Artificial intelligence systems increasingly rely on large volumes of sensitive data to support decision making in domains such as healthcare, finance, education, and public administration. While these systems offer substantial benefits, their growing dependence on personal information has intensified concerns about privacy, data misuse, and loss of public trust. This study examines privacy-preserving artificial intelligence as a design approach that enables meaningful data analysis while limiting exposure of sensitive information. The paper analyses key privacy-preserving techniques, including federated learning, differential privacy, homomorphic encryption, and secure multi party computation, and evaluates their relevance across critical application domains. It also identifies practical challenges related to computational cost, data heterogeneity, regulatory compliance, and explainability. The study shows that privacy-preserving methods can support responsible and trustworthy artificial intelligence when privacy, utility, and governance considerations are addressed together.

With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintaining transparency have encountered many challenges. It includes some such issues as follows: data privacy, centralized vulnerability, inefficiency in fraud detection and much more. To that effect, and to address such limitations, this paper provides a blockchain technology framework that is driven by advanced machine learning techniques, which will enhance security and transparency throughout the network of transactions. We begin with a design framework based on Federated Learning for Blockchain Integration where distributed datasets across blockchain nodes contribute to a global machine learning model but do not share raw data samples. Different nodes learn their own models. After that, these local models are aggregated towards a common, global model using secure aggregation methods, which makes sure that there is nozza of data privacy and hence, in the process making sure that more accurate models can be obtained due to diversified data sets. With LSTMs Autoencoders, more excellent security protocols are created for anomaly detection and fraud. So, by training the autoencoder on normal transaction data, the system can alert transactions with high reconstruction errors, meaning real-time anomalies. This proactive detection of anomalies reduces fraudulent activities significantly as most of the threats are recognized early. To this end, this paper proposes Smart Contract-based Model Management for machine learning models in a decentralized environment. Smart contracts are responsible for the submission, validation, and execution of the locally updated models in a decentralized fashion such that the management process is transparent and tamper resistant. Integrity and authenticity requirements are fulfilled by enforcing consensus mechanisms. Privacy in Machine Learning is guaranteed through Differential Privacy and Homomorphic Encryption. Differential privacy techniques, so as to ensure individual transaction data privacy in the updates of the local model before aggregation. In homomorphic encryption, computations are made in the encrypted form so when forming privacy preserving global model, privacy is preserved. The Real-time analysis of the transactions can be done with CNNs to detect fraud. Streaming transaction data is analyzed by CNNs leveraging the privacy-preserving global model and producing immediate alerts and actions for detected fraud. This real timing makes the network even more reliable and trustworthy. Our proposed framework is effective according to the interim outcomes where the aggregation of local models occurred without data leakage, detected anomalies very efficiently, managed models very transparently, with privacy of data at a very high level, and easily detected fraudulent transactions. The work presented here provides a great boost to send secure and very easily transparent transactions across the network, and thus resulted in enhanced network trust and decentralization.

Deep learning models, particularly those used for im-age classification, have become pervasive in today's technological landscape. However, the increasing demand for privacy, espe-cially when sharing data with external parties, poses significant challenges. Homomorphic encryption (HE) offers a compelling solution by enabling computations on encrypted data without compromising privacy. Despite its potential, the widespread adoption of HE remains limited due to its computational complexity. In this work, we propose an optimized approach to improve the efficiency of a fundamental building block in image classification models: the convolution operation. Our approach is based on the observation that multiple kernel evaluations in a convolution layer can be performed in parallel, provided that their receptive fields do not overlap. Using this property, we reorganize convolution computations into independent groups, significantly reducing redundant operations. This grouping reduces the number of multiplications by up to 75% compared to standard encrypted convolution techniques. Additionally, the approach eliminates the need for costly preprocessing, enabling direct processing of encrypted inputs. To further accelerate homomorphic operations, we leverage a GPU-accelerated HE library, enabling faster op-erations. These contributions make encrypted convolution signifi-cantly more practical for real-world deep learning applications.

This paper addresses the growing necessity of protecting AI models and their underlying data using cryptographic techniques. As AI continues to integrate into critical industries such as healthcare, finance, and autonomous systems, unique vulnerabilities arise that expose models to threats like data poisoning, adversarial attacks, and model inversion. By applying cryptographic methods like homomorphic encryption, differential privacy, and secure multiparty computation, organizations can safeguard both the integrity and confidentiality of AI models and training data. This white paper provides insights into these cryptographic approaches, detailing how each can protect against specific threats while maintaining model performance and compliance.

In recent years, to surmount the challenges of data sensitivity and data silos, federated learning (FL), a collaborative decentralized privacy preservation machine learning framework was proposed and rapidly became a current research hotspot. In this paper, we present an overview of FL workflow, as well as the communication overhead and privacy leakage problems associated with FL. In order to alleviate these two pain points, several corresponding solutions are presented in this paper along with their advantages and disadvantages. To reduce the communication overhead, this paper suggests three reliable approaches by reviewing related research including increasing the amount of computing on the edge side, model compression, and improving parallelism. To address the privacy concerns, this paper introduces three prevailing privacy preservation techniques: differential privacy, homomorphic encryption, and secure multiparty computation (SMC), and shows how to efficiently incorporate these privacy-preserving techniques into a federated learning framework by summarizing relevant literature. On the whole, this paper contributes to explaining the overall workflow of a FL framework and helps advance the understanding of FL in improving communication efficiency and enhancing privacy security.

Private approximate nearest neighbor search for on-chain data based on locality-sensitive hashing

Federated Learning (FL) is a new paradigm in distributed machine learning that allows numerous participants to train a common model without exchanging raw data. This strategy improves data privacy and security, addressing major concerns in sensitive domains including healthcare, banking, and mobile applications. Unlike traditional centralized learning, FL moves model training closer to the data source, reducing data leakage risks and boosting regulatory compliance (e.g., GDPR, HIPAA). This paper provides a complete overview of federated learning, including its core architecture, distinct types (horizontal, vertical, and transfer learning), enabling technologies (such as secure multiparty computation, differential privacy, and homomorphic encryption), and real-world applications. The paper also examines the fundamental issues encountered by FL, such as data heterogeneity, communication overhead, and vulnerability to adversarial assaults. The report concludes with interesting research prospects for increasing model performance, security, and scalability in federated environments.

Federated Learning's (FL) distributed threat detection technique is a significant advancement in cybersecurity as it preserves privacy while processing data in a decentralized manner. Centralized security systems that rely on raw data collection present two major threats to users because they create regulatory problems in addition to data breaches. FL removes security concerns through its model-building process, allowing different organizations to work together without sharing private data. This document investigates FL's role in cybersecurity through an analysis of malware/ransomware detection, IDS applications, secure threat detection, and network traffic anomaly detection. The paper explores effective privacy-protecting techniques: FL implementations are protected against Byzantine and backdoor attacks using Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), Differential Privacy (DP), and Secure Model Aggregation. FL delivers advantages but encounters challenges mainly related to excessive communication demands as well as performance deterioration under adversarial conditions, and difficulties with system expansion. The research provides an exhaustive analysis of FL-based cybersecurity frameworks while discussing existing applications and security threats together with future developments for these systems and the need for advanced privacy-protecting methods to improve the dependability of FL cybersecurity solutions.