Cloud services have turned remote computation into a commodity and enable convenient online collaboration. However, they require that clients fully trust the service provider in terms of confidentiality, integrity, and availability. Towards reducing this dependency, this paper introduces a protocol for verification of integrity and consistency for cloud object storage (VICOS), which enables a group of mutually trusting clients to detect data-integrity and consistency violations for a cloud object-storage service. It aims at services where multiple clients cooperate on data stored remotely on a potentially misbehaving service. VICOS enforces the consistency notion of fork-linearizability, supports wait-free client semantics for most operations, and reduces the computation and communication overhead compared to previous protocols. VICOS is based in a generic way on any authenticated data structure. Moreover, its operations cover the hierarchical name space of a cloud object store, supporting a real-world interface and not only a simplistic abstraction. A prototype of VICOS that works with the key-value store interface of commodity cloud storage services has been implemented, and an evaluation demonstrates its advantage compared to existing systems.
Cryptographic integrity guarantees are of increasing interest for many diverse domains: Verena @cite_27 , for example, is a recent enhancement for web applications that involve database queries and updates by multiple clients. It targets a patient database holding diagnostic data and treatment information. In contrast to , however, it relies on a trusted server that supplies hash values of data objects to clients during every operation.
@cite_27
2518442919 1570343904 1990044789 2019586918
Web applications rely on web servers to protect the integrity of sensitive information. However, an attacker gaining access to web servers can tamper with the data and query computation results, and thus serve corrupted web pages to the user. Violating the integrity of the web page can have serious consequences, affecting application functionality and decision-making processes. Worse yet, data integrity violation may affect physical safety, as in the case of medical web applications which enable physicians to assign treatment to patients based on diagnostic information stored at the web server. This paper presents Verena, a web application platform that provides end-to-end integrity guarantees against attackers that have full access to the web and database servers. In Verena, a client's browser can verify the integrity of a web page by verifying the results of queries on data stored at the server. Verena provides strong integrity properties such as freshness, completeness, and correctness for a common set of database queries, by relying on a small trusted computing base. In a setting where there can be many users with different write permissions, Verena allows a developer to specify an integrity policy for query results based on our notion of trust contexts, and then enforces this policy efficiently. We implemented and evaluated Verena on top of the Meteor framework. Our results show that Verena can support real applications with modest overhead. We formulate and study a privacy guarantee to data owners, who share information with clients by publishing views of a proprietary database. The owner identi.es the sensitive proprietary data using a secret query against the proprietary database. Given an extra view, the privacy guarantee ensures that potential attackers will not learn any information about the secret that could not already be obtained from the existing views. We de.ne “learning” as the modi.cation of the attacker's a-priori probability distribution on the set of possible secrets. We assume arbitrary a-priori distributions (including distributions that correlate the existence of particular tuples) and solve the problem when secret and views are expressed as unions of conjunctive queries with non-equalities, under integrity constraints. We consider guarantees (a) for given view extents (b) for given domain of the secret and (c) independent of the domain and extents. Security of database outsourcing, due to the untrustworthiness of service provider, is a basic challenge to have Database As a Service in a cloud computing environment. Having disparate assumptions to solve different aspects of security such as confidentiality and integrity is an obstacle for an integrated secure solution through the combination of existing approaches. Concentrating on confidentiality and integrity aspects of database outsourcing, this paper proposes an approach in which each attribute value is split up between several data servers using a customized threshold secret sharing scheme. Our approach preserves data confidentiality and at the same time provides the correctness verifiability of query results for clients. The distribution algorithm and redundant shares in the secret sharing scheme are the basis of correctness verification for query results. We propose a privacy protection framework for large-scale content-based information retrieval. It offers two layers of protection. First, robust hash values are used as queries to prevent revealing original content or features. Second, the client can choose to omit certain bits in a hash value to further increase the ambiguity for the server. Due to the reduced information, it is computationally difficult for the server to know the client’s interest. The server has to return the hash values of all possible candidates to the client. The client performs a search within the candidate list to find the best match. Since only hash values are exchanged between the client and the server, the privacy of both parties is protected. We introduce the concept oftunable privacy, where the privacy protection level can be adjusted according to a policy. It is realized through hash-based piecewise inverted indexing. The idea is to divide a feature vector into pieces and index each piece with a subhash value. Each subhash value is associated with an inverted index list. The framework has been extensively tested using a large image database. We have evaluated both retrieval performance and privacy-preserving performance for a particular content identification application. Two different constructions of robust hash algorithms are used. One is based on random projections; the other is based on the discrete wavelet transform. Both algorithms exhibit satisfactory performance in comparison with state-of-the-art retrieval schemes. The results show that the privacy enhancement slightly improves the retrieval performance. We consider the majority voting attack for estimating the query category and identification. Experiment results show that this attack is a threat when there are near-duplicates, but the success rate decreases with the number of omitted bits and the number of distinct items.