Related papers: Privacy-Preserving and Outsourced Multi-User k-Mea…
Big data is a term used for a very large data sets that have many difficulties in storing and processing the data. Analysis this much amount of data will lead to information loss. The main goal of this paper is to share data in a way that…
Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise…
IoT is the fastest-growing technology with a wide range of applications in various domains. IoT devices generate data from a real-world environment every second and transfer it to the cloud due to the less storage at the edge site. An…
We study the problem of differentially private clustering under input-stability assumptions. Despite the ever-growing volume of works on differential privacy in general and differentially private clustering in particular, only three works…
Outsourcing data in the cloud has become nowadays very common. Since -- generally speaking -- cloud data storage and management providers cannot be fully trusted, mechanisms providing the confidentiality of the stored data are necessary. A…
Graph clustering under the framework of differential privacy, which aims to process graph-structured data while protecting individual privacy, has been receiving increasing attention. Despite significant achievements in current research,…
A distributed computing protocol consists of three components: (i) Data Localization: a network-wide dataset is decomposed into local datasets separately preserved at a network of nodes; (ii) Node Communication: the nodes hold individual…
Nowadays processing of Big Security Data, such as log messages, is commonly used for intrusion detection purposed. Its heterogeneous nature, as well as combination of numerical and categorical attributes does not allow to apply the existing…
Computation outsourcing is an integral part of cloud computing. It enables end-users to outsource their computational tasks to the cloud and utilize the shared cloud resources in a pay-per-use manner. However, once the tasks are outsourced,…
In modern distributed computing applications, such as federated learning and AIoT systems, protecting privacy is crucial to prevent adversarial parties from colluding to steal others' private information. However, guaranteeing the utility…
Recent advances in technology have made our work easier compare to earlier times. Computer network is growing day by day but while discussing about the security of computers and networks it has always been a major concerns for organizations…
K-means is one of the most widely used algorithms for clustering in Data Mining applications, which attempts to minimize the sum of the square of the Euclidean distance of the points in the clusters from the respective means of the…
The rapid growth of data from edge devices has catalyzed the performance of machine learning algorithms. However, the data generated resides at client devices thus there are majorly two challenge faced by traditional machine learning…
Outsourcing decision tree inference services to the cloud is highly beneficial, yet raises critical privacy concerns on the proprietary decision tree of the model provider and the private input data of the client. In this paper, we design,…
In the current digital age, the volume of data generated by various cyber activities has become enormous and is constantly increasing. The data may contain valuable insights that can be harnessed to improve cyber security measures. However,…
With the advent of 5G technology that presents enhanced communication reliability and ultra low latency, there is renewed interest in employing cloud computing to perform high performance but computationally expensive control schemes like…
Clustering is an effective technique in data mining to generate groups that are the matter of interest. Among various clustering approaches, the family of k-means algorithms and min-cut algorithms gain most popularity due to their…
Security and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt cloud services. One common approach to address the concerns is client-side encryption where data is encrypted on the client…
Diffusion Models (DMs) achieve state-of-the-art synthesis results in image generation and have been applied to various fields. However, DMs sometimes seriously violate user privacy during usage, making the protection of privacy an urgent…
Clustering non-independent and identically distributed (non-IID) data under local differential privacy (LDP) in federated settings presents a critical challenge: preserving privacy while maintaining accuracy without iterative communication.…