Related papers: Privacy in Data Service Composition
Data mining has made broad significant multidisciplinary field used in vast application domains and extracts knowledge by identifying structural relationship among the objects in large data bases. Privacy preserving data mining is a new…
Federated learning (FL) as distributed machine learning has gained popularity as privacy-aware Machine Learning (ML) systems have emerged as a technique that prevents privacy leakage by building a global model and by conducting…
The main objective of data mining is to extract previously unknown patterns from large collection of data. With the rapid growth in hardware, software and networking technology there is outstanding growth in the amount data collection.…
As the modern world becomes increasingly digitized and interconnected, distributed signal processing has proven to be effective in processing its large volume of data. However, a main challenge limiting the broad use of distributed signal…
We investigate a new form of (privacy-preserving) inconsistency measurement for multi-party communication. Intuitively, for two knowledge bases K_A, K_B (of two agents A, B), our results allow to quantitatively assess the degree of…
Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data…
Social networks have become an essential meeting point for millions of individuals willing to publish and consume huge quantities of heterogeneous information. Some studies have shown that the data published in these platforms may contain…
Under the current regulatory framework for data protections, the protection of human rights writ large and the corresponding outcomes are regulated largely independently from the data and tools that both threaten those rights and are needed…
Differential privacy is a popular privacy-enhancing technology that has been deployed both in industry and government agencies. Unfortunately, existing explanations of differential privacy fail to set accurate privacy expectations for data…
There is an increasing demand to make data "open" to third parties, as data sharing has great benefits in data-driven decision making. However, with a wide variety of sensitive data collected, protecting privacy of individuals, communities…
The problem of preserving privacy when a multivariate source is required to be revealed partially to multiple users is modeled as a Gray-Wyner source coding problem with K correlated sources at the encoder and K decoders in which the kth…
The integration of fairness and privacy in centralized data-driven applications is critical, especially as these systems increasingly influence sectors with significant societal impact. Current methods rarely address privacy, fairness, and…
In our data-centric world, most services rely on collecting and using personal data. The EU's General Data Protection Regulation (GDPR) aims to enhance individuals' control over their data, but its practical impact is not well understood.…
Privacy-preserving machine learning (ML) seeks to balance data utility and privacy, especially as regulations like the GDPR mandate the anonymization of personal data for ML applications. Conventional anonymization approaches often reduce…
One of major considerations in an online business is customer privacy. Consumers are not interested in being monitored and identified by sellers. Some solutions are proposed to hide selection of the customer but in the payment phase, there…
Today's world is a globalized and connected one, where people are increasingly moving around and interacting with a greater number of services and devices of all kinds, including those that allow them to monitor their health. However, each…
Consumers frequently interact with reputation systems to rate products, services, and deliveries. While past research extensively studied different conceptual approaches to realize such systems securely and privacy-preservingly, these…
The massive growth of the Internet of Things (IoT) as a network of interconnected entities [18], brings up new challenges in terms of privacy and security requirements to the traditional software engineering domain [4]. To protect the…
Data privacy is one of the key challenges faced by enterprises today. Anonymization techniques address this problem by sanitizing sensitive data such that individual privacy is preserved while allowing enterprises to maintain and share…
The widespread adoption of continuously connected smartphones and tablets developed the usage of mobile applications, among which many use location to provide geolocated services. These services provide new prospects for users: getting…