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Internet of Things (IoT) is slowly but steadily becoming part of different aspects of our lives, with its applications ranging from smart homes, to wearable devices, to healthcare, etc. This wide spectrum of applications results in shared data containing large amount of users' private information. The security of such information becomes a paramount concern. The IoT security requirements include data...
Outsourcing computation to cloud server has recently become popular in cloud computing. Cloud computing technologies enable clients with limited computational resources to outsource their massive computations to powerful cloud servers. Outsourcing computation has some new concerns, such as outsourced data and result privacy, verifiability and efficiency. Matrix multiplication is one of the most basic...
Various communication protocols are currently used in the Internet of Things (IoT) devices. One of the protocols that are already standardized by ISO is MQTT protocol (ISO / IEC 20922: 2016). Many IoT developers use this protocol because of its minimal bandwidth requirement and low memory consumption. Sometimes, IoT device sends confidential data that should only be accessed by authorized people or...
Despite years of research and the long-lasting promise of pervasiveness of an "Internet of Things", it is only recently that a truly convincing number of connected things have been deployed in the wild. New services are now being built on top of these things and allow to realize the IoT vision.However, integration of things in complex and interconnected systems is still only in the hands...
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...
The postMessage feature in HTML5 allows web components of different origins to communicate with each other. However, the message receivers do not differentiate the origins of a message, making information leakage possible. Being aware of this vulnerability, We examine its implication under the context of Single Sign On (SSO) mechanism. Nowadays, many websites integrate SSO to facilitate easier user...
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...
With the prevalence of cloud computing, privacy-preserving database outsourcing has been spotlighted. To preserve both data privacy and query privacy from adversaries, databases need to be encrypted before being outsourced to the cloud. However, there exists the only kNN classification scheme over the encrypted databases in the cloud. Because the existing scheme suffers from high computation overhead,...
In recent years, data is becoming the most valuable asset. There are more and more data exchange markets on Internet. These markets help data owners publish their datasets and data consumers find appropriate services. However, different from traditional goods like clothes and food, data is a special commodity. For current data exchange markets, it is very hard to protect copyright and privacy. Moreover,...
Outsource encrypted data has attracted attentions from industry and academics for storing sensitive data in third party clouds. Many cloud applications need privacy preserving multiple keywords search services over encrypted data with dual capabilities. On one hand, they need to keep the query keywords and associated search operations private such that data hosting service providers cannot trace and...
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 a public cloud, a data owner outsources encrypted data in order to protect its privacy. However, when sharing data with particular users, it is neither willing to share the decryption key nor reluctant to download and decrypt data. The former may reveal its identity and the latter can cause high computing and communication cost. In this paper, we proposed a half-decryption scheme (SDS2) for crowd...
Time series discords are subsequences of a longer time series that are maximally different to all the rest of the time series subsequences. Recent advancements in sensor technology have made it possible to collect enormous amounts of data in real time, sometimes, the unilateral data can't support the data mining task, you must consider the multi-party situation, combining multi-party data into a complete...
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