Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201

Building an Authentication and Quality of Query Services in the Cloud

AbstractCloud outpouring is careful when an information distributor has given sensitive data to a set of trusted agents and few o f the information is leaked and found in an unauthorized place. An enterprise data leak may be a scary proposition. Security practitioners always deal with data cloud leakage issues that arise from various ways like e-mail and different net channels. In case of information cloud leakage from trusted agents, the distributor should assess the probability that the leaked information came from one or more agents.The proposed system can identify those parties who are guilty for such cloud leakage even once the data is altered. For this the system will use data allocation ways are also can inject “realistic but fake” information records improve the identification of cloud leakage. Moreover, data can also be leaked from inside an organization through e-mail. Hence there's also a need to filter these e-mails, may be done by blocking e-mails that contain pictures, videos or sensitive data in an organization. A principle utilized in e-mail filtering is classify e-mail based mostly the fingerprints of message bodies, the white and black lists of mail addresses and the words specified to spam

Preserving Data Confidentiality and Query Privacy Using KNN-R Approach

Cloud computing is one of the famous and well known technique that processes the data query efficiently. Since it is maintaining huge amount of resources, its privacy and security is an issue. Cloud service providers are not trust worthy, so data is to be secured. Whenever the data is sent to the cloud, it is encrypted because to protect the sensitive data such that query privacy and data confidentiality is assured. Cloud computing reduces the in- house resources .This doesn’t mean processing of the query should be slow. To ensure query privacy and data confidentiality RASP approach is designed. The RASP Perturbation technique combines Order preserving Encryption, Dimensionality Expansion, random noise injection , random projection to provide strong safety to the perturbed data and query. RASP make use of the kNN algorithm to process the query efficiently. kNN approach use the minimum square range to process the query. It transfers data to the multidimensional space where it uses indexing approach to process the minimum square range points. DOI: 10.17762/ijritcc2321-8169.150313

DESIGN AND IMPLEMENTATION OF RASP IN DATA COMMOTION IN BUILDING ARCANE AND CAPABILITY QUERY SERVICES

Because of distinctive advantages in scalability and price-saving, storing of understanding-intensive query services in cloud gets increasingly popular. We setup random space perturbation system to produce realistic range query and k nearest-neighbour services of query in cloud. The forecasted approach will undertake data confidentiality, privacy of query, efficient processing of query furthermore to lessen in-house price of processing, and obtain a great balance within it. Random space perturbation system is a kind of growing perturbation, by mixture of order preserving file encryption, random noise injection, and random project. Random space perturbation system encloses lots of significant features. The fundamental proposal should be to at random modify complex data sets by grouping of order preserving file encryption, random noise injection, random project and dimensionality expansion, to make sure that utility for handing range queries is preserved

Instant Privacy-Preserving Biometric Authentication for Hamming Distance

In recent years, there has been enormous research attention in privacy-preserving biometric authentication, which enables a user to verify him or herself to a server without disclosing raw biometric information. Since biometrics is irrevocable when exposed, it is very important to protect its privacy. In IEEE TIFS 2018, Zhou and Ren proposed a privacy-preserving user-centric biometric authentication scheme named PassBio, where the end-users encrypt their own templates, and the authentication server never sees the raw templates during the authentication phase. In their approach, it takes about 1 second to encrypt and compare 2000-bit templates based on Hamming distance on a laptop. However, this result is still far from practice because the size of templates used in commercialized products is much larger: according to NIST IREX IX report of 2018 which analyzed 46 iris recognition algorithms, size of their templates varies from 4,632-bit (579-byte) to 145,832-bit (18,229-byte). In this paper, we propose a new privacy-preserving user-centric biometric authentication (HDM-PPBA) based on Hamming distance, which shows a big improvement in efficiency to the previous works. It is based on our new single-key function-hiding inner product encryption, which encrypts and computes the Hamming distance of 145,832-bit binary in about 0.3 seconds on Intel Core i5 2.9GHz CPU. We show that it satisfies simulation-based security under the hardness assumption of Learning with Errors (LWE) problem. The storage requirements, bandwidth and time complexity of HDM-PPBA depend linearly on the bit-length of biometrics, and it is applicable to any large templates used in NIST IREX IX report with high efficiency

Cryptographic Techniques for Securing Data in the Cloud

El paradigma de la computació al núvol proporciona accés remot a potents infraestructures a cost reduït. Tot i que l’adopció del núvol ofereix nombrosos beneficis, la migració de dades sol requerir un alt nivell de confiança en el proveïdor de serveis i introdueix problemes de privacitat. En aquesta tesi es dissenyen tècniques per a permetre a usuaris del núvol protegir un conjunt de dades externalitzades. Les solucions proposades emanen del projecte H2020 de la Comissió Europea “CLARUS: User-Centered Privacy and Security in the Cloud”. Els problemes explorats són la cerca sobre dades xifrades, la delegació de càlculs d’interpolació, els esquemes de compartició de secrets i la partició de dades. Primerament, s’estudia el problema de la cerca sobre dades xifrades mitjançant els esquemes de xifrat cercable simètric (SSE), i es desenvolupen tècniques que permeten consultes per rangs dos-dimensionals a SSE. També es tracta el mateix problema utilitzant esquemes de xifrat cercable de clau pública (PEKS), i es presenten esquemes PEKS que permeten consultes conjuntives i de subconjunt. En aquesta tesi també s’aborda la delegació privada de computacions Kriging. Kriging és un algoritme d’interpolació espaial dissenyat per a aplicacions geo-estadístiques. Es descriu un mètode per a delegar interpolacions Kriging de forma privada utilitzant xifrat homomòrfic. Els esquemes de compartició de secrets són una primitiva fonamental en criptografia, utilitzada a diverses solucions orientades al núvol. Una de les mesures d’eficiència relacionades més importants és la taxa d’informació òptima. Atès que calcular aquesta taxa és generalment difícil, s’obtenen propietats que faciliten la seva descripció. Finalment, es tracta el camp de la partició de dades per a la protecció de la privacitat. Aquesta tècnica protegeix la privacitat de les dades emmagatzemant diversos fragments a diferents ubicacions. Aquí s’analitza aquest problema des d’un punt de vista combinatori, fitant el nombre de fragments i proposant diversos algoritmes.El paradigma de la computación en la nube proporciona acceso remoto a potentes infraestructuras a coste reducido. Aunque la adopción de la nube ofrece numerosos beneficios, la migración de datos suele requerir un alto nivel de confianza en el proveedor de servicios e introduce problemas de privacidad. En esta tesis se diseñan técnicas para permitir a usuarios de la nube proteger un conjunto de datos externalizados. Las soluciones propuestas emanan del proyecto H2020 de la Comisión Europea “CLARUS: User-Centered Privacy and Security in the Cloud”. Los problemas explorados son la búsqueda sobre datos cifrados, la delegación de cálculos de interpolación, los esquemas de compartición de secretos y la partición de datos. Primeramente, se estudia el problema de la búsqueda sobre datos cifrados mediante los esquemas de cifrado simétrico buscable (SSE), y se desarrollan técnicas para permitir consultas por rangos dos-dimensionales en SSE. También se trata el mismo problema utilizando esquemas de cifrado buscable de llave pública (PEKS), y se presentan esquemas que permiten consultas conyuntivas y de subconjunto. Adicionalmente, se aborda la delegación privada de computaciones Kriging. Kriging es un algoritmo de interpolación espacial diseñado para aplicaciones geo-estadísticas. Se describe un método para delegar interpolaciones Kriging privadamente utilizando técnicas de cifrado homomórfico. Los esquemas de compartición de secretos son una primitiva fundamental en criptografía, utilizada en varias soluciones orientadas a la nube. Una de las medidas de eficiencia más importantes es la tasa de información óptima. Dado que calcular esta tasa es generalmente difícil, se obtienen propiedades que facilitan su descripción. Por último, se trata el campo de la partición de datos para la protección de la privacidad. Esta técnica protege la privacidad de los datos almacenando varios fragmentos en distintas ubicaciones. Analizamos este problema desde un punto de vista combinatorio, acotando el número de fragmentos y proponiendo varios algoritmos.The cloud computing paradigm provides users with remote access to scalable and powerful infrastructures at a very low cost. While the adoption of cloud computing yields a wide array of benefits, the act of migrating to the cloud usually requires a high level of trust in the cloud service provider and introduces several security and privacy concerns. This thesis aims at designing user-centered techniques to secure an outsourced data set in cloud computing. The proposed solutions stem from the European Commission H2020 project “CLARUS: User-Centered Privacy and Security in the Cloud”. The explored problems are searching over encrypted data, outsourcing Kriging interpolation computations, secret sharing and data splitting. Firstly, the problem of searching over encrypted data is studied using symmetric searchable encryption (SSE) schemes, and techniques are developed to enable efficient two-dimensional range queries in SSE. This problem is also studied through public key encryption with keyword search (PEKS) schemes, efficient PEKS schemes achieving conjunctive and subset queries are proposed. This thesis also aims at securely outsourcing Kriging computations. Kriging is a spatial interpolation algorithm designed for geo-statistical applications. A method to privately outsource Kriging interpolation is presented, based in homomorphic encryption. Secret sharing is a fundamental primitive in cryptography, used in many cloud-oriented techniques. One of the most important efficiency measures in secret sharing is the optimal information ratio. Since computing the optimal information ratio of an access structure is generally hard, properties are obtained to facilitate its description. Finally, this thesis tackles the privacy-preserving data splitting technique, which aims at protecting data privacy by storing different fragments of data at different locations. Here, the data splitting problem is analyzed from a combinatorial point of view, bounding the number of fragments and proposing various algorithms to split the data

Function-specific schemes for verifiable computation

An integral component of modern computing is the ability to outsource data and computation to powerful remote servers, for instance, in the context of cloud computing or remote file storage. While participants can benefit from this interaction, a fundamental security issue that arises is that of integrity of computation: How can the end-user be certain that the result of a computation over the outsourced data has not been tampered with (not even by a compromised or adversarial server)? Cryptographic schemes for verifiable computation address this problem by accompanying each result with a proof that can be used to check the correctness of the performed computation. Recent advances in the field have led to the first implementations of schemes that can verify arbitrary computations. However, in practice the overhead of these general-purpose constructions remains prohibitive for most applications, with proof computation times (at the server) in the order of minutes or even hours for real-world problem instances. A different approach for designing such schemes targets specific types of computation and builds custom-made protocols, sacrificing generality for efficiency. An important representative of this function-specific approach is an authenticated data structure (ADS), where a specialized protocol is designed that supports query types associated with a particular outsourced dataset. This thesis presents three novel ADS constructions for the important query types of set operations, multi-dimensional range search, and pattern matching, and proves their security under cryptographic assumptions over bilinear groups. The scheme for set operations can support nested queries (e.g., two unions followed by an intersection of the results), extending previous works that only accommodate a single operation. The range search ADS provides an exponential (in the number of attributes in the dataset) asymptotic improvement from previous schemes for storage and computation costs. Finally, the pattern matching ADS supports text pattern and XML path queries with minimal cost, e.g., the overhead at the server is less than 4% compared to simply computing the result, for all our tested settings. The experimental evaluation of all three constructions shows significant improvements in proof-computation time over general-purpose schemes

Applying Secure Multi-party Computation in Practice

In this work, we present solutions for technical difficulties in deploying secure multi-party computation in real-world applications. We will first give a brief overview of the current state of the art, bring out several shortcomings and address them. The main contribution of this work is an end-to-end process description of deploying secure multi-party computation for the first large-scale registry-based statistical study on linked databases. Involving large stakeholders like government institutions introduces also some non-technical requirements like signing contracts and negotiating with the Data Protection Agency