Biblio

Users leave when page loads take too long. This simple fact has complex implications for virtually all modern businesses, because accelerating content delivery through caching is not as simple as it used to be. As a fundamental technical challenge, the high degree of personalization in today's Web has seemingly outgrown the capabilities of traditional content delivery networks (CDNs) which have been designed for distributing static assets under fixed caching times. As an additional legal challenge for services with personalized content, an increasing number of regional data protection laws constrain the ways in which CDNs can be used in the first place. In this paper, we present Speed Kit as a radically different approach for content distribution that combines (1) a polyglot architecture for efficiently caching personalized content with (2) a natively GDPR-compliant client proxy that handles all sensitive information within the user device. We describe the system design and implementation, explain the custom cache coherence protocol to avoid data staleness and achieve Δ-atomicity, and we share field experiences from over a year of productive use in the e-commerce industry.

Differential privacy, which is due to its rigorous mathematical proof and strong privacy guarantee, has become a standard for the release of statistics with privacy protection. Recently, a lot of dynamic data publishing algorithms based on differential privacy have been proposed, but most of the algorithms use a native method to allocate the privacy budget. That is, the limited privacy budget is allocated to each time point uniformly, which may result in the privacy budget being unreasonably utilized and reducing the utility of data. In order to make full use of the limited privacy budget in the dynamic data publishing and improve the utility of data publishing, we propose a dynamic data publishing algorithm based on reinforcement learning in this paper. The algorithm consists of two parts: privacy budget allocation and data release. In the privacy budget allocation phase, we combine the idea of reinforcement learning and the changing characteristics of dynamic data, and establish a reinforcement learning model for the allocation of privacy budget. Finally, the algorithm finds a reasonable privacy budget allocation scheme to publish dynamic data. In the data release phase, we also propose a new dynamic data publishing strategy to publish data after the privacy budget is exhausted. Extensive experiments on real datasets demonstrate that our algorithm can allocate the privacy budget reasonably and improve the utility of dynamic data publishing.

Cloud storage can provide outsourcing data services for both organizations and individuals. However, cloud storage still faces many challenges, e.g., public integrity auditing, the support of dynamic data, and low computational audit cost. To solve the problems, a number of techniques have been proposed. Recently, Tian et al. proposed a novel public auditing scheme for secure cloud storage based on a new data structure DHT. The authors claimed that their scheme was proven to be secure. Unfortunately, through our security analysis, we find that the scheme suffers from one attack and one security shortage. The attack is that an adversary can forge the data to destroy the correctness of files without being detected. The shortage of the scheme is that the updating operations for data blocks is vulnerable and easy to be modified. Finally, we give our countermeasures to remedy the security problems.

Cloud computing is a new computing paradigm which encourages remote data storage. This facility shoots up the necessity of secure data auditing mechanism over outsourced data. Several mechanisms are proposed in the literature for supporting dynamic data. However, most of the existing schemes lack the security feature, which can withstand collusion attacks between the cloud server and the abrogated users. This paper presents a technique to overthrow the collusion attacks and the data auditing mechanism is achieved by means of vector commitment and backward unlinkable verifier local revocation group signature. The proposed work supports multiple users to deal with the remote cloud data. The performance of the proposed work is analysed and compared with the existing techniques and the experimental results are observed to be satisfactory in terms of computational and time complexity.

Cloud service providers offer storage outsourcing facility to their clients. In a secure cloud storage (SCS) protocol, the integrity of the client's data is maintained. In this work, we construct a publicly verifiable secure cloud storage protocol based on a secure network coding (SNC) protocol where the client can update the outsourced data as needed. To the best of our knowledge, our scheme is the first SNC-based SCS protocol for dynamic data that is secure in the standard model and provides privacy-preserving audits in a publicly verifiable setting. Furthermore, we discuss, in details, about the (im)possibility of providing a general construction of an efficient SCS protocol for dynamic data (DSCS protocol) from an arbitrary SNC protocol. In addition, we modify an existing DSCS scheme (DPDP I) in order to support privacy-preserving audits. We also compare our DSCS protocol with other SCS schemes (including the modified DPDP I scheme). Finally, we figure out some limitations of an SCS scheme constructed using an SNC protocol.