Federated Learning (FL) is a privacy-preserving technique that allows clients to collaboratively train models while keeping their local data private from both other clients and the central server. However, the presence of a malicious server or attacker can lead to model inversion attacks, which compromise clients’ private information. To counteract these threats, homomorphic encryption (HE) has been proposed as a solution, enabling encrypted model aggregation by the server without exposing raw data. This ensures that a malicious server or attacker cannot access model parameters or reveal clients’ local information. However, HE introduces substantial computational and communication overhead, making it impractical for many real-world implementations. To address this, recent research suggests selectively encrypting only a subset of most important model parameters while leaving the rest unencrypted. While it is promising, the challenge lies in identifying these key parameters to maintain security while minimizing the additional costs associated with homomorphic operations. In addition, even with a subset of parameters, none of the existing methods offer truly efficient and practical HE-based solution. In general, these approaches fall short when applied to large, complex deep learning models and are impractical beyond basic implementations. This paper introduces a straightforward yet effective parameter selection and encryption strategy for HE-based federated learning. Our goal is to minimize this computational, and communication overhead caused by homomorphic operations while maintaining robust security, ensuring practical applicability in real-world scenarios. We begin by evaluating different implementations of CKKS (Cheon-Kim-Kim-Song)-based HE algorithm (which allows floating point operations on encrypted domain) across various model architectures, systematically analyzing the additional burden HE imposes in terms of processing time and size of the model parameters. Next, we introduce a magnitude-based selective encryption strategy that not only provide same security guarantees compared to existing methods but is also practical for scaling to larger, more complex models in FL environments. Hence, this approach enables developers to make informed decisions that strike an optimal balance between performance and privacy/security, making it feasible for real-world deployments.

For over many decades, relational database model has been considered as the leading model for data storage and management. However, as the Big Data explosion has generated a large volume of data, alternative models like NoSQL and NewSQL have emerged. With the advancement of communication technology, these database systems have given the potential to change the existing architecture from centralized mechanism to distributed in nature, to deploy as cloud-based solutions. Though all of these evolving technologies mostly focus on performance guarantees, it is still being a major concern how these systems can ensure the security and privacy of the information they handle. Different datastores support different types of integrated security mechanisms, however, most of the non-relational database systems have overlooked the security requirements of modern Big Data applications. This paper reviews security implementations in today's leading database models giving more emphasis on security and privacy attributes. A set of standard security mechanisms have been identified and evaluated based on different security classifications. Further, it provides a thorough review and a comprehensive analysis on maturity of security and privacy implementations in these database models along with future directions/enhancements so that data owners can decide on most appropriate datastore for their data-driven Big Data applications.

Community detection is of great importance for online social network analysis. The volume, variety and velocity of data generated by today's online social networks are advancing the way researchers analyze those networks. For instance, real-world networks, such as Facebook, LinkedIn and Twitter, are inherently growing rapidly and expanding aggressively over time. However, most of the studies so far have been focusing on detecting communities on the static networks. It is computationally expensive to directly employ a well-studied static algorithm repeatedly on the network snapshots of the dynamic networks. We propose DynaMo, a novel modularity-based dynamic community detection algorithm, aiming to detect communities of dynamic networks as effective as repeatedly applying static algorithms but in a more efficient way. DynaMo is an adaptive and incremental algorithm, which is designed for incrementally maximizing the modularity gain while updating the community structure of dynamic networks. In the experimental evaluation, a comprehensive comparison has been made among DynaMo, Louvain (static) and 5 other dynamic algorithms. Extensive experiments have been conducted on 6 real-world networks and 10,000 synthetic networks. Our results show that DynaMo outperforms all the other 5 dynamic algorithms in terms of the effectiveness, and is 2 to 5 times (by average) faster than Louvain algorithm.

Supervised learning techniques such as classification algorithms learn from training data to predict the correct label for newly presented input data. In many real-world scenarios, training data required by such techniques can contain personal information and data collection can be a significant problem due to privacy concerns. Cryptographic techniques have been used before to do training on encrypted data. However, such techniques are computationally expensive and they are not scalable most of the time. If a dataset in another party will be used for training, differential privacy technology can be used to preserve the privacy of the individuals in the dataset. When there is no such dataset and data needs to be collected from individuals directly for training, local differential privacy can be used. Local differential privacy is a technology to preserve privacy during data sharing with an untrusted data collector. In this work, we propose to use local differential privacy techniques to train a Naive Bayes classifier. Using the proposed solution, an untrusted party collects perturbed data from individuals that keep the relationship between the feature values and class labels. By estimating probabilities needed by the Naive Bayes classifier using the perturbed data, the untrusted party can classify new instances with high accuracy. We develop solutions that work for both discrete and continuous data. We also propose utilizing dimensionality reduction techniques to decrease communication cost and improve accuracy. We show the accuracy of the proposed Naive Bayes classifier achieving local differential privacy via experiments on several datasets. We also show how dimensionality reduction enhances the accuracy.