Property-preserving Encryption Research Articles

C3PO: C loud-based C onfidentiality-preserving C ontinuous Query P r o cessing

With the advent of the Internet of things (IoT), billions of devices are expected to continuously collect and process sensitive data (e.g., location, personal health factors). Due to the limited computational capacity available on IoT devices, the current de facto model for building IoT applications is to send the gathered data to the cloud for computation. While building private cloud infrastructures for handling large amounts of data streams can be expensive, using low-cost public (untrusted) cloud infrastructures for processing continuous queries including sensitive data leads to strong concerns over data confidentiality. This article presents C3PO, a confidentiality-preserving, continuous query processing engine, that leverages the public cloud. The key idea is to intelligently utilize partially homomorphic and property-preserving encryption to perform as many computationally intensive operations as possible—without revealing plaintext—in the untrusted cloud. C3PO provides simple abstractions to the developer to hide the complexities of applying complex cryptographic primitives, reasoning about the performance of such primitives, deciding which computations can be executed in an untrusted tier, and optimizing cloud resource usage. An empirical evaluation with several benchmarks and case studies shows the feasibility of our approach. We consider different classes of IoT devices that differ in their computational and memory resources (from a Raspberry Pi 3 to a very small device with a Cortex-M3 microprocessor) and through the use of optimizations, we demonstrate the feasibility of using partially homomorphic and property-preserving encryption on IoT devices.

SegGuard: Segmentation-based Anonymization of Network Data in Clouds for Privacy-Preserving Security Auditing

Security auditing allows cloud tenants to verify the compliance of cloud infrastructure with respect to desirable security properties, e.g., whether a tenant’s virtual network is properly isolated from other tenants’ networks. However, the input to the auditing task, such as the detailed topology of the underlying cloud infrastructure, typically contains sensitive information which a cloud provider may be reluctant to hand over to a third party auditor. Additionally, auditing results intended for one tenant may inadvertently reveal private information about other tenants, e.g., another tenant’s VM is reachable due to a misconfiguration. How to anonymize both the input data and the auditing results in order to prevent such information leakage is a novel challenge that has received little attention. Directly applying most of the existing anonymization techniques to such a context would either lead to insufficient protection or render the data unsuitable for auditing. In this article, we propose SegGuard , a novel anonymization approach that prevents cross-tenant information leakage through per-tenant encryption, and prevents information leakage to auditors through hiding real input segments among fake ones; in addition, applying property-preserving encryption in an innovative way enables SegGuard to preserve the data utility for auditing while mitigating semantic attacks. We implement SegGuard based on OpenStack, and evaluate its effectiveness and overhead using both synthetic and real data. Our experimental results demonstrate that SegGuard can reduce the information leakage to a negligible level (e.g., less than 1 percent for an adversary with 50 percent pre-knowledge) with a practical response time (e.g., 62 seconds to anonymize a cloud infrastructure with 25,000 virtual machines).

CloudDBGuard: A framework for encrypted data storage in NoSQL wide column stores

Nowadays, cloud storage providers are widely used for outsourcing data. These remote cloud servers are not trustworthy when storing sensitive data. In this article we focus on the use case of storing data in a cloud database using a particular sub-category of NoSQL databases — so-called wide column stores. Unfortunately security was not a primary concern of the NoSQL systems designers. Using encryption before outsourcing the data can provide security. Conventional encryption however limits the options for interaction because the encrypted data lacks properties of the plaintext data that the database systems rely on. Various schemes have been proposed for property-preserving encryption in order to overcome these issues, allowing a database to process queries over encrypted data. In this article we comprehensively present details of our framework CloudDBGuard that allows using property-preserving encryption in unmodified wide column stores. It hides the complexity of the encryption and decryption process and allows various adjustments on specific use cases in order to achieve a maximum of security, functionality and performance.

Методы организации произвольных конфиденциальных запросов к криптографической облачной базе данных с помощью полностью гомоморфного шифрования

The processing of queries to an encrypted database without its decryption has been actively investigated recently by both cryptographers and database researchers. Such a work is allowed by various types of so-called Processable encryption (PE), as well as special architectures of database management systems (DBMS) which use these types of encryption. The most known types of PEs are order-preserving encryption, homomorphic encryption, functional encryption, searchable encryption, and property-preserving encryption. Based on these types of encryption, various DBMSs are built, the most famous of which are CryptDB, Mo- nomi, Arx and DBMS by researchers from Novosibirsk. These DBMSs are built on the basis of various types of PEs, for example order-preserving encryption, homomorphic encryption and traditional block encryption. However, this approach can cause privacy problems. The best approach from the security viewpoint is to build a cryptographic database using only homomorphic encryption. An obstacle to this is insufficient efficiency of the existing homomorphic encryption schemes and incomplete solution of a set of issues related to ensuring the confidentiality of decisions making in an untrusted environment. In this paper, we propose the techniques for solving these problems, in particular for organization of execution arbitrary secure query to the encrypted relational database using fully homomorphic encryption. Also we propose a model of query condition that splits query into atomic predicates and linking condition. One of roposed technique is aimed at ensuring the security of linking condition of queries, others keep security of atomic predicates. The parameters of the proposed techniques make it possible to implement them using the already existing homomorphic encryption schemes. The proposed techniques can be a basis for building secure cryptographic cloud databases.

Cryptanalytic study of property-preserving encryption

Cryptanalytic study of property-preserving encryption