In recent years, federated learning has attracted more and attention as it could collaboratively train a global model without gathering the users' raw data. It brought many challenges. this paper, we proposed layer-based system with privacy preservation. We successfully reduced communication cost by selecting several layers of to upload for averaging enhanced protection applying local different...

5G-enabled drones have potential applications in a variety of both military and civilian settings (e.g., monitoring tracking individuals demonstrations and/or enforcing social/ physical distancing during pandemics such as COVID-19). Such generally involve the collection dissemination (massive) data from to remote centers for storage analysis via 5G networks). Consequently, there are security pr...

The traditional paradigm in machine learning has been that given a data set, the goal is to learn a target function or decision model (such as a classifier) from it. Many techniques in data mining and machine learning follow a gradient descent paradigm in the iterative process of discovering this target function or decision model. For instance, Linear regression can be resolved through a gradie...

Social networks are in great expansion nowadays. Opposite to many benefits, such as fast access to information, simple interaction among users, or being a perspective data source for decision makers and analysts, they raise many problems related to privacy protection. Vast amount of personal information that are voluntarily provided is disclosed in social networks and prone to misuse. Therefore...

In recent year’s privacy preservation in data mining has become an important issue. A new class of data mining method called privacy preserving data mining algorithm has been developed. The aim of these algorithms is to protect the sensitive information in data while extracting knowledge from large amount of data. We focus the general classification in a secured manner and introduce a privacy-p...

In this paper we study privacy preservation for the publication of search engine query logs. In particular, we introduce a new privacy concern, which is that of website privacy (or business privacy). We define the possible adversaries that could be interested in disclosing website information and the vulnerabilities found in the query log, from which they could benefit. In this work we also det...

Spatial generalization has been recently proposed as a technique for the anonymization of requests in location based services. This paper provides a formal characterization of a privacy attack that has been informally described in previous work, and presents a new generalization algorithm that is proved to be a safe defense against that attack. The paper also reports the results of an extensive...

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In this paper, some privacy-preserving features for distributed subgradient optimization algorithms are considered. Most of the existing distributed algorithms focus mainly on the algorithm design and convergence analysis, but not the protection of agents' privacy. Privacy is becoming an increasingly important issue in applications involving sensitive information. In this paper, we first show t...