Attribute based encryption (ABE) is an encrypted access control mechanism that ensures efficient data sharing among dynamic group of users. Nevertheless, this encryption technique presents two main drawbacks, namely high decryption cost and publicly shared access policies, thus leading to possible users' privacy leakage. In this paper, we introduce PHOABE, a Policy-Hidden Outsourced ABE scheme. Our construction presents several advantages. First, it is a multi-attribute authority ABE scheme. Second, the expensive computations for the ABE decryption process is partially delegated to a Semi Trusted Cloud Server. Third, users' privacy is protected thanks to a hidden access policy. Fourth, PHOABE is proven to be selectively secure, verifiable and policy privacy preserving under the random oracle model. Five, estimation of the processing overhead proves its feasibility in IoT constrained environments.
This paper presents an anonymous certification (AC) scheme, built over an attribute based signature (ABS). After identifying properties and core building blocks of anonymous certification schemes, we identify ABS limitations to fulfill AC properties, and we propose a new system model along with a concrete mathematical construction based on standard assumptions and the random oracle model. Our solution has several advantages. First, it provides a data minimization cryptographic scheme, permitting the user to reveal only required information to any service provider. Second, it ensures unlinkability between the different authentication sessions, while preserving the anonymity of the user. Third, the derivation of certified attributes by the issuing authority relies on a non interactive protocol which provides an interesting communication overhead.
Recent years have witnessed the trend of increasingly relying on distributed infrastructures. This increased the number of reported incidents of security breaches compromising users' privacy, where third parties massively collect, process and manage users' personal data. Towards these security and privacy challenges, we combine hierarchical identity based cryptographic mechanisms with emerging blockchain infrastructures and propose a blockchain-based data usage auditing architecture ensuring availability and accountability in a privacy-preserving fashion. Our approach relies on the use of auditable contracts deployed in blockchain infrastructures. Thus, it offers transparent and controlled data access, sharing and processing, so that unauthorized users or untrusted servers cannot process data without client's authorization. Moreover, based on cryptographic mechanisms, our solution preserves privacy of data owners and ensures secrecy for shared data with multiple service providers. It also provides auditing authorities with tamper-proof evidences for data usage compliance.
Personal data are often collected and processed in a decentralized fashion, within different contexts. For instance, with the emergence of distributed applications, several providers are usually correlating their records, and providing personalized services to their clients. Collected data include geographical and indoor positions of users, their movement patterns as well as sensor-acquired data that may reveal users' physical conditions, habits and interests. Consequently, this may lead to undesired consequences such as unsolicited advertisement and even to discrimination and stalking. To mitigate privacy threats, several techniques emerged, referred to as Privacy Enhancing Technologies, PETs for short. On one hand, the increasing pressure on service providers to protect users' privacy resulted in PETs being adopted. One the other hand, service providers have built their business model on personalized services, e.g. targeted ads and news. The objective of the paper is then to identify which of the PETs have the potential to satisfy both usually divergent -economical and ethical -purposes. This paper identifies a taxonomy classifying eight categories of PETs into three groups, and for better clarity, it considers three categories of personalized services. After defining and presenting the main features of PETs with illustrative examples, the paper points out which PETs best fit each personalized service category. Then, it discusses some of the inter-disciplinary privacy challenges that may slow down the adoption of these techniques, namely: technical, social, legal and economic concerns. Finally, it provides recommendations and highlights several research directions.
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