摘要 :
The evolution of the Information and Communication Technology has radically changed our electronic lives, making information the key driver for today's society. Every action we perform requires the collection, elaboration, and dis...
展开
The evolution of the Information and Communication Technology has radically changed our electronic lives, making information the key driver for today's society. Every action we perform requires the collection, elaboration, and dissemination of personal information. This situation has clearly brought a tremendous exposure of private and sensitive information to privacy breaches.In this chapter, we describe how the techniques developed for protecting data have evolved in the years. We start by providing an overview of the first privacy definitions (k-anonymity, l-diversity, t-closeness, and their extensions) aimed at ensuring proper data protection against identity and attribute disclosures. We then illustrate how changes in the underlying assumptions lead to scenarios characterized by different and more complex privacy requirements. In particular, we show the impact on privacy when considering multiple releases of the same data or dynamic data collections, fine-grained privacy definitions, generic privacy constraints, and the external knowledge that a potential adversary may exploit for inferring sensitive information. We also briefly present the concept of differential privacy that has recently emerged as an alternative privacy definition.
收起
摘要 :
The evolution of the Information and Communication Tech-nology has radically changed our electronic lives, making information the key driver for today's society. Every action we perform requires the collection, elaboration, and di...
展开
The evolution of the Information and Communication Tech-nology has radically changed our electronic lives, making information the key driver for today's society. Every action we perform requires the collection, elaboration, and dissemination of personal information. This situation has clearly brought a tremendous exposure of private and sen-sitive information to privacy breaches.In this chapter, we describe how the techniques developed for protect-ing data have evolved in the years. We start by providing an overview of the first privacy definitions (k-anonymity, l-diversity, t-closeness, and their extensions) aimed at ensuring proper data protection against iden-tity and attribute disclosures. We then illustrate how changes in the underlying assumptions lead to scenarios characterized by different and more complex privacy requirements. In particular, we show the impact on privacy when considering multiple releases of the same data or dy-namic data collections, fine-grained privacy definitions, generic privacy constraints, and the external knowledge that a potential adversary may exploit for inferring sensitive information. We also briefly present the concept of differential privacy that has recently emerged as an alternaI ve privacy definition.
收起
摘要 :
Cloud computing has emerged as a successful paradigm allowing individual users as well as companies to resort to external providers for storing/processing data or making them available to others. Together with the many benefits, c...
展开
Cloud computing has emerged as a successful paradigm allowing individual users as well as companies to resort to external providers for storing/processing data or making them available to others. Together with the many benefits, cloud computing introduces however new security and privacy risks. A major issue is that the data owner, storing data at external providers, loses control over them, leaving them potentially exposed to improper access, use, or dissemination. In this chapter, we consider the problem of protecting confidentiality of sensitive information when relying on external cloud providers for storing and processing data. We introduce confidentiality requirements and then illustrate encryption and data fragmentation as possible protection techniques. In particular, we discuss different approaches that have been proposed using encryption (with indexing) and fragmentation, either by themselves or in combination, to satisfy confidentiality requirements.
收起
摘要 :
Cloud computing has emerged as a successful paradigm allowing individual users as well as companies to resort to external providers for storing/processing data or making them available to others. Together with the many benefits, c...
展开
Cloud computing has emerged as a successful paradigm allowing individual users as well as companies to resort to external providers for storing/processing data or making them available to others. Together with the many benefits, cloud computing introduces however new security and privacy risks. A major issue is that the data owner, storing data at external providers, loses control over them, leaving them potentially exposed to improper access, use, or dissemination. In this chapter, we consider the problem of protecting confidentiality of sensitive information when relying on external cloud providers for storing and processing data. We introduce confidentiality requirements and then illustrate encryption and data fragmentation as possible protection techniques. In particular, we discuss different approaches that have been proposed using encryption (with indexing) and fragmentation, either by themselves or in combination, to satisfy confidentiality requirements.
收起
摘要 :
The availability of a multitude of data sources has naturally increased the need for subjects to collaborate for distributed computations, aimed at combining different data collections for their elaboration and analysis. Due to th...
展开
The availability of a multitude of data sources has naturally increased the need for subjects to collaborate for distributed computations, aimed at combining different data collections for their elaboration and analysis. Due to the quick pace at which collected data grow, often the authorities collecting and owning such datasets resort to external third parties (e.g., cloud providers) for their storage and management. Data under the control of different authorities are autonomously encrypted (using a different encryption scheme and key) for their external storage. This makes distributed computations combining these sources hard. In this paper, we propose an approach enabling collaborative computations over data encrypted in storage, selectively involving also subjects that might not be authorized for accessing the data in plaintext when it is considered economically convenient.
收起
摘要 :
The availability of a multitude of data sources has naturally increased the need for subjects to collaborate for distributed computations, aimed at combining different data collections for their elaboration and analysis. Due to th...
展开
The availability of a multitude of data sources has naturally increased the need for subjects to collaborate for distributed computations, aimed at combining different data collections for their elaboration and analysis. Due to the quick pace at which collected data grow, often the authorities collecting and owning such datasets resort to external third parties (e.g., cloud providers) for their storage and management. Data under the control of different authorities are autonomously encrypted (using a different encryption scheme and key) for their external storage. This makes distributed computations combining these sources hard. In this paper, we propose an approach enabling collaborative computations over data encrypted in storage, selectively involving also subjects that might not be authorized for accessing the data in plaintext when it is considered economically convenient.
收起
摘要 :
The shuffle index provides confidentiality guarantees for accesses to externally outsourced data. In this paper, we extend the shuffle index with support for access control, that is, for enforcing authorizations on data. Our appro...
展开
The shuffle index provides confidentiality guarantees for accesses to externally outsourced data. In this paper, we extend the shuffle index with support for access control, that is, for enforcing authorizations on data. Our approach bases on the use of selective encryption and on the organization of data and authorizations in two shuffle indexes. Our proposal enables owners to regulate access to their data supporting authorizations allowing different users access to different portions of the data, while at the same time guaranteeing confidentiality of access.
收起
摘要 :
The shuffle index provides confidentiality guarantees for accesses to externally outsourced data. In this paper, we extend the shuffle index with support for access control, that is, for enforcing authorizations on data. Our appro...
展开
The shuffle index provides confidentiality guarantees for accesses to externally outsourced data. In this paper, we extend the shuffle index with support for access control, that is, for enforcing authorizations on data. Our approach bases on the use of selective encryption and on the organization of data and authorizations in two shuffle indexes. Our proposal enables owners to regulate access to their data supporting authorizations allowing different users access to different portions of the data, while at the same time guaranteeing confidentiality of access.
收起
摘要 :
The shuffle index has been recently proposed for organizing and accessing data in outsourcing scenarios while protecting the confidentiality of the data as well as of the accesses to them. In this paper, we extend the shuffle inde...
展开
The shuffle index has been recently proposed for organizing and accessing data in outsourcing scenarios while protecting the confidentiality of the data as well as of the accesses to them. In this paper, we extend the shuffle index to the use of multiple servers for storing data, introducing a new protection technique (shadow) and enriching the original ones by operating in a distributed scenario. Our distributed shuffle index produces a significant increase in the protection of the system, with no additional costs.
收起
摘要 :
The shuffle index has been recently proposed for organizing and accessing data in outsourcing scenarios while protecting the confidentiality of the data as well as of the accesses to them. In this paper, we extend the shuffle inde...
展开
The shuffle index has been recently proposed for organizing and accessing data in outsourcing scenarios while protecting the confidentiality of the data as well as of the accesses to them. In this paper, we extend the shuffle index to the use of multiple servers for storing data, introducing a new protection technique (shadow) and enriching the original ones by operating in a distributed scenario. Our distributed shuffle index produces a significant increase in the protection of the system, with no additional costs.
收起
原文获取