30,557 research outputs found

    Accountable privacy preserving attribute based framework for authenticated encrypted access in clouds

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    In this paper, we propose an accountable privacy preserving attribute-based framework, called Ins-PAbAC, that combines attribute based encryption and attribute based signature techniques for securely sharing outsourced data contents via public cloud servers. The proposed framework presents several advantages. First, it provides an encrypted access control feature, enforced at the data owner’s side, while providing the desired expressiveness of access control policies. Second, Ins-PAbAC preserves users’ privacy, relying on an anonymous authentication mechanism, derived from a privacy preserving attribute based signature scheme that hides the users’ identifying information. Furthermore, our proposal introduces an accountable attribute based signature that enables an inspection authority to reveal the identity of the anonymously-authenticated user if needed. Third, Ins-PAbAC is provably secure, as it is resistant to both curious cloud providers and malicious users adversaries. Finally, experimental results, built upon OpenStack Swift testbed, point out the applicability of the proposed scheme in real world scenarios

    Bicameral and Auditably Private Signatures

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    This paper introduces Bicameral and Auditably Private Signatures (BAPS) -- a new privacy-preserving signature system with several novel features. In a BAPS system, given a certified attribute x\mathbf{x} and a certified policy PP, a signer can issue a publicly verifiable signature ÎŁ\Sigma on a message mm as long as (m,x)(m, \mathbf{x}) satisfies PP. A noteworthy characteristic of BAPS is that both attribute x\mathbf{x} and policy PP are kept hidden from the verifier, yet the latter is convinced that these objects were certified by an attribute-issuing authority and a policy-issuing authority, respectively. By considering bicameral certification authorities and requiring privacy for both attributes and policies, BAPS generalizes the spirit of existing advanced signature primitives with fine-grained controls on signing capabilities (e.g., attribute-based signatures, predicate signatures, policy-based signatures). Furthermore, BAPS provides an appealing feature named auditable privacy, allowing the signer of ÎŁ\Sigma to verifiably disclose various pieces of partial information about PP and x\mathbf{x} when asked by auditor(s)/court(s) at later times. Auditable privacy is intrinsically different from and can be complementary to the notion of accountable privacy traditionally incorporated in traceable anonymous systems such as group signatures. Equipped with these distinguished features, BAPS can potentially address interesting application scenarios for which existing primitives do not offer a direct solution. We provide rigorous security definitions for BAPS, following a ``sim-ext\u27\u27 approach. We then demonstrate a generic construction based on commonly used cryptographic building blocks, which employs a sign-then-commit-then-prove design. Finally, we present a concrete instantiation of BAPS, that is proven secure in the random oracle model under lattice assumptions. The scheme can handle arbitrary policies represented by polynomial-size Boolean circuits and can address quadratic disclosing functions. In the construction process, we develop a new technical building block that could be of independent interest: a zero-knowledge argument system allowing to prove the satisfiability of a certified-and-hidden Boolean circuit on certified-and-committed inputs

    Anonymous reputation based reservations in e-commerce (AMNESIC)

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    Online reservation systems have grown over the last recent years to facilitate the purchase of goods and services. Generally, reservation systems require that customers provide some personal data to make a reservation effective. With this data, service providers can check the consumer history and decide if the user is trustable enough to get the reserve. Although the reputation of a user is a good metric to implement the access control of the system, providing personal and sensitive data to the system presents high privacy risks, since the interests of a user are totally known and tracked by an external entity. In this paper we design an anonymous reservation protocol that uses reputations to profile the users and control their access to the offered services, but at the same time it preserves their privacy not only from the seller but the service provider

    Privacy and security protection in cloud integrated sensor networks

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    Wireless sensor networks have been widely deployed in many social settings to monitor human activities and urban environment. In these contexts, they acquire and collect sensory data, and collaboratively fuse the data. Due to resource constraint, sensor nodes however cannot perform complex data processing. Hence, cloud-integrated sensor networks have been proposed to leverage the cloud computing capabilities for processing vast amount of heterogeneous sensory data. After being processed, the sensory data can then be accessed and shared among authorized users and applications pervasively. Various security and privacy threats can arise when the people-centric sensory data is collected and transmitted within the sensor network or from the network to the cloud; security and privacy remain a big concern when the data is later accessed and shared among different users and applications after being processed. Extensive research has been conducted to address the security and privacy issues without sacrificing resource efficiency. Unfortunately, the goals of security/privacy protection and resource efficiency may not be easy to accomplish simultaneously, and may even be sharply contrary to each other. Our research aims to reconcile the conflicts between these goals in several important contexts. Specifically, we first investigate the security and privacy protection of sensory data being transmitted within the sensor network or from the sensor network to the cloud, which includes: (1) efficient, generic privacy preserving schemes for sensory data aggregation; (2) a privacy-preserving integrity detection scheme for sensory data aggregation; (3) an efficient and source-privacy preserving scheme for catching packet droppers and modifiers. Secondly, we further study how to address people\u27s security and privacy concerns when accessing sensory data from the cloud. To preserve privacy for sensory data aggregation, we propose a set of generic, efficient and collusion-resilient privacy-preserving data aggregation schemes. On top of these privacy preserving schemes, we also develop a scheme to simultaneously achieve privacy preservation and detection of integrity attack for data aggregation. Our approach outperforms existing solutions in terms of generality, node compromise resilience, and resource efficiency. To remove the negative effects caused by packet droppers and modifiers, we propose an efficient scheme to identify and catch compromised nodes which randomly drop packets and/or modify packets. The scheme employs an innovative packet marking techniques, with which selective packet dropping and modification can be significantly alleviated while the privacy of packet sources can be preserved. To preserve the privacy of people accessing the sensory data in the cloud, we propose a new efficient scheme for resource constrained devices to verify people\u27s access privilege without exposing their identities in the presence of outsider attacks or node compromises; to achieve the fine-grained access control for data sharing, we design privacy-preserving schemes based on users\u27 affiliated attributes, such that the access policies can be flexibly specified and enforced without involving complicated key distribution and management overhead. Extensive analysis, simulations, theoretical proofs and implementations have been conducted to evaluate the effectiveness and efficiency of our proposed schemes. The results show that our proposed schemes resolve several limitations of existing work and achieve better performance in terms of resource efficiency, security strength and privacy preservation

    Oblivion: Mitigating Privacy Leaks by Controlling the Discoverability of Online Information

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    Search engines are the prevalently used tools to collect information about individuals on the Internet. Search results typically comprise a variety of sources that contain personal information -- either intentionally released by the person herself, or unintentionally leaked or published by third parties, often with detrimental effects on the individual's privacy. To grant individuals the ability to regain control over their disseminated personal information, the European Court of Justice recently ruled that EU citizens have a right to be forgotten in the sense that indexing systems, must offer them technical means to request removal of links from search results that point to sources violating their data protection rights. As of now, these technical means consist of a web form that requires a user to manually identify all relevant links upfront and to insert them into the web form, followed by a manual evaluation by employees of the indexing system to assess if the request is eligible and lawful. We propose a universal framework Oblivion to support the automation of the right to be forgotten in a scalable, provable and privacy-preserving manner. First, Oblivion enables a user to automatically find and tag her disseminated personal information using natural language processing and image recognition techniques and file a request in a privacy-preserving manner. Second, Oblivion provides indexing systems with an automated and provable eligibility mechanism, asserting that the author of a request is indeed affected by an online resource. The automated ligibility proof ensures censorship-resistance so that only legitimately affected individuals can request the removal of corresponding links from search results. We have conducted comprehensive evaluations, showing that Oblivion is capable of handling 278 removal requests per second, and is hence suitable for large-scale deployment

    Foundations of Fully Dynamic Group Signatures

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    Group signatures allow members of a group to anonymously sign on behalf of the group. Membership is administered by a designated group manager. The group manager can also reveal the identity of a signer if and when needed to enforce accountability and deter abuse. For group signatures to be applicable in practice, they need to support fully dynamic groups, i.e., users may join and leave at any time. Existing security definitions for fully dynamic group signatures are informal, have shortcomings, and are mutually incompatible. We fill the gap by providing a formal rigorous security model for fully dynamic group signatures. Our model is general and is not tailored toward a specific design paradigm and can therefore, as we show, be used to argue about the security of different existing constructions following different design paradigms. Our definitions are stringent and when possible incorporate protection against maliciously chosen keys. We consider both the case where the group management and tracing signatures are administered by the same authority, i.e., a single group manager, and also the case where those roles are administered by two separate authorities, i.e., a group manager and an opening authority. We also show that a specialization of our model captures existing models for static and partially dynamic schemes. In the process, we identify a subtle gap in the security achieved by group signatures using revocation lists. We show that in such schemes new members achieve a slightly weaker notion of traceability. The flexibility of our security model allows to capture such relaxation of traceability
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