Wireless sensor networks (WSNs) are ad-hoc networks composed of tiny devices with limited computation and energy capacities. For such devices, data transmission is a very energy-consuming operation. It thus becomes essential to the lifetime of a WSN to minimize the number of bits sent by each device. One wellknown approach is to aggregate sensor data (e.g., by adding) along the path from sensors to the sink. Aggregation becomes especially challenging if end-to-end privacy between sensors and the sink is required. In this paper, we propose a simple and provably secure additively homomorphic stream cipher that allows efficient aggregation of encrypted data. The new cipher only uses modular additions (with very small moduli) and is therefore very well suited for CPU-constrained devices. We show that aggregation based on this cipher can be used to efficiently compute statistical values such as mean, variance and standard deviation of sensed data, while achieving significant bandwidth gain.

Wireless sensor networks (WSNs) are composed of tiny devices with limited computation and battery capacities. For such resource-constrained devices, data transmission is a very energy-consuming operation. To maximize WSN lifetime, it is essential to minimize the number of bits sent and received by each device. One natural approach is to aggregate sensor data along the path from sensors to the sink. Aggregation is especially challenging if end-to-end privacy between sensors and the sink (or aggregate integrity) is required. In this article, we propose a simple and provably secure encryption scheme that allows efficient additive aggregation of encrypted data. Only one modular addition is necessary for ciphertext aggregation. The security of the scheme is based on the indistinguishability property of a pseudorandom function (PRF), a standard cryptographic primitive. We show that aggregation based on this scheme can be used to efficiently compute statistical values, such as mean, variance, and standard deviation of sensed data, while achieving significant bandwidth savings. To protect the integrity of the aggregated data, we construct an end-to-end aggregate authentication scheme that is secure against outsider-only attacks, also based on the indistinguishability property of PRFs.

In the Outsourced Database (ODB) model, entities outsource their data management needs to a third-party service provider. Such a service provider offers mechanisms for its clients to create, store, update, and access (query) their databases. This work provides mechanisms to ensure data integrity and authenticity for outsourced databases. Specifically, this article provides mechanisms that assure the querier that the query results have not been tampered with and are authentic (with respect to the actual data owner). It investigates both the security and efficiency aspects of the problem and constructs several secure and practical schemes that facilitate the integrity and authenticity of query replies while incurring low computational and communication costs.

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Abstract. In the Database-As-a-Service (DAS) model, clients store their database contents at servers belonging to potentially untrusted service providers. To maintain data confidentiality, clients need to outsource their data to servers in encrypted form. At the same time, clients must still be able to execute queries over encrypted data. One prominent and fairly effective technique for executing SQL-style range queries over encrypted data involves partitioning (or bucketization) of encrypted attributes.However, executing aggregation-type queries over encrypted data is a notoriously difficult problem. One well-known cryptographic tool often utilized to support encrypted aggregation is homomorphic encryption; it enables arithmetic operations over encrypted data. One technique based on a specific homomorphic encryption function was recently proposed in the context of the DAS model. Unfortunately, as shown in this paper, this technique is insecure against ciphertext-only attacks. We propose a simple alternative for handling encrypted aggregation queries and describe its implementation. We also consider a different flavor of the DAS model which involves mixed databases, where some attributes are encrypted and some are left in the clear. We show how range queries can be executed in this model.