Software applications continue to challenge user privacy when users interact with them. privacy practices (e.g. data Minimisation (DM), privacy by Design (PbD) or General data Protection Regulation (GDPR)) and related...
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ISBN:
(纸本)9781450381284
Software applications continue to challenge user privacy when users interact with them. privacy practices (e.g. data Minimisation (DM), privacy by Design (PbD) or General data Protection Regulation (GDPR)) and related "privacy engineering" methodologies exist and provide clear instructions for developers to implement privacy into software systems they develop that preserve user privacy. However, those practices and methodologies are not yet a common practice in the software development community. There has been no previous research focused on developing "educational" interventions such as serious games to enhance software developers' coding behaviour. Therefore, this research proposes a game design framework as an educational tool for software developers to improve (secure) coding behaviour, so they can develop privacy-preserving software applications that people can use. The elements of the proposed framework were incorporated into a gaming application scenario that enhances the software developers' coding behaviour through their motivation. The proposed work not only enables the development of privacy-preserving software systems but also helping the software development community to put privacy guidelines and engineering methodologies into practice.
Climate change and migration have become one of the most challenging problems for our civilization. In this context, city councils work hard to manage essential services for citizens such as waste collection, street l...
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ISBN:
(纸本)9781450391634
Climate change and migration have become one of the most challenging problems for our civilization. In this context, city councils work hard to manage essential services for citizens such as waste collection, street lamp lighting, and water supply. Increasingly, digitalization and the Internet of Things (IoT) help cities improve services, increase productivity and reduce costs. However, to understand how this may happen, we explore the urban sensing capabilities from citizen- to city-scale, how sensing at different levels is interlinked, and the challenges of managing innovations based on IoT data and *** authorities collaborate with researchers and deploy testbeds as a part of demonstration and research projects to perform the above data collection, improve city services, and support innovation. The data gathered is about indoor and outdoor environmental conditions, energy usage, built environment, structural health monitoring. Such monitoring requires IT infrastructure at three different tiers: at the endpoint, edge, and cloud. Managing infrastructure at all tiers with provisioning, connectivity, security updates of devices, user dataprivacy controls, visualization of data, multi-tenancy of applications, and network resilience, is challenging. So, in turn, we focus on performing a systematic study of the technical and non-technical challenges faced during the implementation, management, and deployment of devices into citizens' homes and public *** third piece of work explores IoT edge applications' resiliency and reliability requirements that vary from non-critical (best delivery efforts) to safety-critical with time-bounded guarantees. We investigate how to meet IoT application mixed-criticality QoS requirements in multi-communication ***, to demonstrate the principles of our framework in the real world, we implement an open-source air quality platform Open City Air Quality Platform (OpenCAQP), that merges a wide range of data sourc
Anonymization is a method used in privacy-preserving data publishing. Previous studies show that anonymization based on the request of a data recipient, the priority of attributes, helps to maintain data utility. Howe...
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ISBN:
(纸本)9781450360999
Anonymization is a method used in privacy-preserving data publishing. Previous studies show that anonymization based on the request of a data recipient, the priority of attributes, helps to maintain data utility. However, it is difficult for recipients to generate requests because they can not know which attribute important without data analysis. To address this issue, we propose a framework for performing custom-made anonymization by data analysis program provided by recipient. This enables the recipient to generate a request after creating a program and performing an indirect analysis of an original dataset by the program. Moreover, we describe an inference attack model for this framework and propose a secure method for restraining such an attack.
Attribute-based encryption (ABE) schemes and their variations are often applied to preserve the privacy of data. In particular, ABE schemes proposals are resilient to multiple attacks, including attacks in interceptio...
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ISBN:
(纸本)9781450383677
Attribute-based encryption (ABE) schemes and their variations are often applied to preserve the privacy of data. In particular, ABE schemes proposals are resilient to multiple attacks, including attacks in interception, interruption, modification, fabrication, unauthorized authentication, and access of data. Existing proposals have several limitations, such as the generation, verification, and distribution of digital certificates incur extra computation and communication overhead which are not suitable for resource-constrained computing. Furthermore, in most of the ABE schemes, a certification authority (CA) generates the public/secret keys according to a set of attributes. However, the compromise of CA can endanger the secret keys, therefore, the secrecy of encrypted messages. Some of the existing ABE schemes are based on bilinear pairing that requires large security parameters, which make ABE schemes unsuitable for resource-constrained computing devices. The current ABE proposals [1, 2, 3, 4] are complex because they require implementing large-number security parameters (i.e., 2048-bit or 4096-bit size) to achieve 2128 security. Besides that, those ABE schemes consider a CA with an active role in the application process. The CA generates and distributes secret keys to devices or users. Nonetheless, sharing private attributes with the CA can risk data and user privacy, since the CA can also decrypt messages, depending on the application scenario, and retrieve the data. Moreover, the compromise of CA poses a risk to the communication secrecy between the sender and the receiver. In addition, some studies propose symmetric key schemes for resource-constrained devices. However, in large-scale networked systems, the symmetric key management becomes very complex and inefficient. The symmetric-key deployment often requires a separate protocol for session key agreement and generation. In IoT networks where mostly short-sized data is exchanged, symmetric key encryption sche
Event monitoring and detection in real-time systems is crucial. Protecting users' data while reporting an event in almost real-time will increase the level of this challenge. In this work, we adopt the strong noti...
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ISBN:
(纸本)9781450360999
Event monitoring and detection in real-time systems is crucial. Protecting users' data while reporting an event in almost real-time will increase the level of this challenge. In this work, we adopt the strong notion of differential privacy to private stream counting for event detection with the aim of minimizing false positive and false negative rates as our utility metrics.
data sharing among partners-users, companies, organizations-is crucial for the advancement of collaborative machine learning in many domains such as healthcare, finance, and security. Sharing through secure computatio...
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ISBN:
(纸本)9781450360999
data sharing among partners-users, companies, organizations-is crucial for the advancement of collaborative machine learning in many domains such as healthcare, finance, and security. Sharing through secure computation and other means allow these partners to perform privacy-preserving computations on their private data in controlled ways. However, in reality, there exist complex relationships among members (partners). Politics, regulations, interest, trust, data demands and needs prevent members from sharing their complete data. Thus, there is a need for a mechanism to meet these conflicting relationships on data sharing. This paper presents CURIE1, an approach to exchange data among members who have complex relationships. A novel policy language, CPL, that allows members to define the specifications of data exchange requirements is introduced. With CPL, members can easily assert who and what to exchange through their local policies and negotiate a global sharing agreement. The agreement is implemented in a distributed privacy-preserving model that guarantees sharing among members will comply with the policy as negotiated. The use of CURIE is validated through an example healthcare application built on recently introduced secure multi-party computation and differential privacy frameworks, and policy and performance trade-offs are explored.
Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This poster continues along the emerging trend in secure data processing that recognize...
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ISBN:
(纸本)9781450360999
Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This poster continues along the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome some of the limitations of existing encryption-based approaches. In particular, this poster outlines a new secure keyword search approach, called query keyword binning (QB) that allows non-sensitive parts of the data to be outsourced in clear-text while guaranteeing that no information is leaked by joint processing of non-sensitive data (in clear-text) and sensitive data (in encrypted form). QB improves the performance of and strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.
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