We present sec-cs, a hash-table-like data structure for contents on untrusted storage that is provably secure and storage-efficient. We achieve authenticity and confidentiality with zero storage overhead using deterministic authenticated encryption. State-of-the-art data deduplication approaches prevent redundant storage of shared parts of different contents irrespective of whether relationships between contents are known a priori.,,,,Instead of just adapting existing approaches, we introduce novel (multi-level) chunking strategies, ML-SC and ML-CDC, which are significantly more storage-efficient than existing approaches in presence of high redundancy.,,,,We prove sec-cs's security, publish an implementation, and present evaluation results indicating suitability for, e.g., future backup systems that should preserve many versions of files on little available storage.
ADS, in general, is an umbrella term for data structures involving three parties (a trusted who publishes data, an untrusted that stores structured data, and a that requests data) that enable authenticated, efficient queries to the data. @cite_33 In this sense, |sec-cs| can be considered an ADS with additional data deduplication and confidentiality properties: The cloud storage backend can be considered the and the user client plays the roles of and . An overview of existing ADS and methods for constructing ADS in general are provided by @cite_26 and @cite_19 .
@cite_19 @cite_26 @cite_33
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An authenticated data structure (ADS) is a data structure whose operations can be carried out by an untrusted prover, the results of which a verifier can efficiently check as authentic. This is done by having the prover produce a compact proof that the verifier can check along with each operation's result. ADSs thus support outsourcing data maintenance and processing tasks to untrusted servers without loss of integrity. Past work on ADSs has focused on particular data structures (or limited classes of data structures), one at a time, often with support only for particular operations. This paper presents a generic method, using a simple extension to a ML-like functional programming language we call λ• (lambda-auth), with which one can program authenticated operations over any data structure defined by standard type constructors, including recursive types, sums, and products. The programmer writes the data structure largely as usual and it is compiled to code to be run by the prover and verifier. Using a formalization of λ• we prove that all well-typed λ• programs result in code that is secure under the standard cryptographic assumption of collision-resistant hash functions. We have implemented λ• as an extension to the OCaml compiler, and have used it to produce authenticated versions of many interesting data structures including binary search trees, red-black+ trees, skip lists, and more. Performance experiments show that our approach is efficient, giving up little compared to the hand-optimized data structures developed previously. Web publishers frequently integrate third-party advertisements into web pages that also contain sensitive publisher data and end-user personal data. This practice exposes sensitive page content to confidentiality and integrity attacks launched by advertisements. In this paper, we propose a novel framework for addressing security threats posed by third-party advertisements. The heart of our framework is an innovative isolation mechanism that enables publishers to transparently interpose between advertisements and end users. The mechanism supports finegrained policy specification and enforcement, and does not affect the user experience of interactive ads. Evaluation of our framework suggests compatibility with several mainstream ad networks, security from many threats from advertisements and acceptable performance overheads. Internet advertising is one of the most popular online business models. JavaScript-based advertisements (ads) are often directly embedded in a web publisher's page to display ads relevant to users (e.g., by checking the user's browser environment and page content). However, as third-party code, the ads pose a significant threat to user privacy. Worse, malicious ads can exploit browser vulnerabilities to compromise users' machines and install malware. To protect users from these threats, we propose AdSentry, a comprehensive confinement solution for JavaScript-based advertisements. The crux of our approach is to use a shadow JavaScript engine to sandbox untrusted ads. In addition, AdSentry enables flexible regulation on ad script behaviors by completely mediating its access to the web page (including its DOM) without limiting the JavaScript functionality exposed to the ads. Our solution allows both web publishers and end users to specify access control policies to confine ads' behaviors. We have implemented a proof-of-concept prototype of AdSentry that transparently supports the Mozilla Firefox browser. Our experiments with a number of ads-related attacks successfully demonstrate its practicality and effectiveness. The performance measurement indicates that our system incurs a small performance overhead. Online advertisements (ads) provide a powerful mechanism for advertisers to effectively target Web users. Ads can be customized based on a user's browsing behavior, geographic location, and personal interests. There is currently a multi-billion dollar market for online advertising, which generates the primary revenue for some of the most popular websites on the Internet. In order to meet the immense market demand, and to manage the complex relationships between advertisers and publishers (i.e., the websites hosting the ads), marketplaces known as "ad exchanges" are employed. These exchanges allow publishers (sellers of ad space) and advertisers(buyers of this ad space) to dynamically broker traffic through ad networks to efficiently maximize profits for all parties. Unfortunately, the complexities of these systems invite a considerable amount of abuse from cybercriminals, who profit at the expense of the advertisers. In this paper, we present a detailed view of how one of the largest ad exchanges operates and the associated security issues from the vantage point of a member ad network. More specifically, we analyzed a dataset containing transactions for ingress and egress ad traffic from this ad network. In addition, we examined information collected from a command-and-control server used to operate a botnet that is leveraged to perpetrate ad fraud against the same ad exchange.