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Statistical clustering plays an important role in data analysis and is one of the most widely used data mining methods. Concerns about the security and privacy of analyzing modernday massive data across distributed networks have prompted the development of privacy preserving data mining algorithms. This paper proposes a scheme for model-based clustering and classification through a privacy-preserving...
With the rapid technological advances in parallel imaging reconstruction, Magnetic Resonance Imaging (MRI) has been increasingly popularized for clinical diagnosis. Among the state-of-the-art approaches, the Simultaneous Auto-calibrating and k-space Estimation (SAKE) realizes a calibration-free reconstruction with high-quality results. However, its reconstruction procedures are still time-consuming...
Online shopping is one of the most important applications on the Internet and it is one that has been steadily growing over the last decade. With increasing numbers of online shopping transactions there are also raising concerns over privacy and protection of the customer data collected by the webshops. This is why, we need privacy-preserving technologies for online shopping, in the interest of both...
Sharing and working on sensitive data in distributed settings from healthcare to finance is a major challenge due to security and privacy concerns. Secure multiparty computation (SMC) is a viable panacea for this, allowing distributed parties to make computations while the parties learn nothing about their data, but the final result. Although SMC is instrumental in such distributed settings, it does...
In many systems the privacy of users depends on the number of participants applying collectively some method to protect their security. Indeed, there are numerous already classic results about revealing aggregated data from a set of users. Apart from data aggregation, it has been noticed that in a wider context privacy can be often reduced to being hidden in a crowd. Generally, the problems is how...
In this paper, we consider the privacy preserving problem of consensus protocol. First, we introduce a privacy preserving scheme, where each node produces and transmits a sequence of random values with their mean equaling to the node's initial state. We show that the network can reach average consensus with privacy preserving scheme, and provide a sufficient condition under which the initial state...
This paper presents a framework for constructing a hierarchical categorical clustering algorithm on horizontal and vertical partitioned dataset. It is assumed that data is distributed between two parties, such that for general benefits both are willing to detect the clusters on whole dataset, but for privacy concerns, they refuse to share the original datasets. To this end, we propose algorithms based...
Cloud storage providers can reduce storage costs by detecting identical files and storing only one instance of them. While appealing to the storage providers, this deduplication set-up raises various privacy concerns among clients. Various techniques to retrofit content confidentiality in deduplication have been studied in the literature. Nevertheless, data encryption alone is insufficient to protect...
Data-driven business processes are gaining popularity among enterprises now-a-days. In many situations, multiple parties would share data towards a common goal if it were possible to simultaneously protect the privacy of the individuals and organizations described in the data. Existing solutions for multi-party analytics require parties to transfer their raw data to a trusted mediator, who then performs...
This paper presents a novel framework for privacy aware collaborative information sharing for data classification. Data holders participating in this information sharing system, for global benefits are interested to model a classifier on whole dataset, but are ready to share their own table of data if a certain amount of privacy is guaranteed. To address this issue, we propose a privacy mechanism...
In this paper, we design a distributed platform for anonymized dataset trading without any centralized trusted third party. The platform consists of peers and consensus-based blockchain mechanism, and each peer acts as a data broker, data receiver, or verifier for blockchain in a data transfer transaction. A data broker collects data from data owners under their consent for data trading. The Privacy...
With the increasing prevalence of cloud computing, data owners prefer to outsource their databases to the cloud. For the protection of data privacy, sensitive data have to be encrypted before outsourcing, which introduces much difficulty into effective data utilization. Most previous studies either suffer from privacy disclosure and low efficiency, or do not support personalized multidimensional range...
Recent large-scale deployments of differentially private algorithms employ the local model for privacy (sometimes called PRAM or randomized response), where data are randomized on each individual's device before being sent to a server that computes approximate, aggregate statistics. The server need not be trusted for privacy, leaving data control in users' hands. For an important class of convex optimization...
Personal information is often gathered and processed in a decentralized fashion. Examples include health records and governmental data bases. To protect the privacy of individuals, no unique user identifier should be used across the different databases. At the same time, the utility of the distributed information needs to be preserved which requires that it be nevertheless possible to link different...
This fast abstract proposes a privacy-preserving quantum-secure protocol for DBSCAN clustering algorithm. It allows multiple parties to jointly compute clusters without revealing their individual datasets to each other. We show how to outsource the DBSCAN computation on encrypted data to a cloud service provider securely such that the parties do not need to perform the expensive clustering computation...
We consider a problem where multiple agents participate in solving a quadratic optimization problem subject to linear inequality constraints in a privacy-preserving manner. Several variables of the objective function as well as the constraints are privacy-sensitive and are known to different agents. We propose a privacy-preserving protocol based on partially homomorphic encryption where each agent...
The growth of the Internet of Things (IoT) creates the possibility of decentralized systems of sensing and actuation, potentially on a global scale. IoT devices connected to cloud networks can offer sensing and actuation as a service enabling networks of sensors to grow to a global scale. But extremely large sensor networks can violate privacy, especially in the case where IoT devices are mobile and...
With the development of intelligent transport systems (ITS) and vehicular ad hoc network (VANET), vehicular cloud computing (VCC) has been proposed to bring essential and potential benefits, such as improving traffic safety and offering computational services to road users. To make such computational services reliable and secure, the computation results from the vehicular cloud (VC) should be verifiable...
The integration, mining, and analysis of person-specific data can provide enormous opportunities for organizations, governments, and researchers to leverage today's massive data collections. However, the use of personal or otherwise sensitive data also raises concerns about the privacy, confidentiality, and potential discrimination of people. Privacy-preserving record linkage (PPRL) is a growing research...
Privacy is considered as one of the hottest issues in nowadays communication research areas. It focuses on protecting the content of the transmitted data and on preserving the contextual information such as the identity of the communicating entities. This paper presents an overview of research done in this area in order to build a foundation that helps the research community to understand more the...
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