Efficient and provably secure aggregation of encrypted data in wireless sensor networks

Abstract

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.

Efficient Privacy-Preserving Stream Aggregation in Mobile Sensing with Low Aggregation Error

“…As suggested in [17,20], it can be implemented with HMAC, where f s (t) is the HMAC of t with s as the key. Function h maps the output of f s to a uniform random value in [0, M − 1].…”

“…There are many existing works (e.g., [17]) on privacy-preserving data aggregation, but most of them assume a trusted aggregator. Shi et al [18] proposed an aggregation scheme for mobile sensing, but the scheme does not consider time-series data.…”

“…Shi et al [18] proposed an aggregation scheme for mobile sensing, but the scheme does not consider time-series data. The two constructions [19,20] based on additive homomorphic encryption [17] do not guarantee differential privacy.…”

Private Computation of Spatial and Temporal Power Consumption with Smart Meters

“…Examples of techniques that compute the sum of multiple private inputs include [7,5], where encryption is done by modular addition (each player simply adds its key to the plaintext) and aggregation is very efficient, also performed via addition. However, this approach assumes a semi-trusted aggregator who knows the sum of all keys (for each reporting interval) and can thus decrypt the aggregated value by subtraction.…”

Secure and energy-efficient data aggregation in Wireless Sensor Networks

“…A very simple, yet elegant approach for privacy-preserving multi-party computation is proposed by Chaum [20]. Castellucia et al have described how inexpensive encryption operations may be exploited in aggregation schemes for WSNs to ensure data privacy [5].…”