This paper presents a new privacy-preserving smart metering system. Our scheme is private under the differential privacy model and therefore provides strong and provable guarantees. With our scheme, an (electricity) supplier can periodically collect data from smart meters and derive aggregated statistics while learning only limited information about the activities of individual households. For example, a supplier cannot tell from a user's trace when he watched TV or turned on heating. Our scheme is simple, efficient and practical. Processing cost is very limited: smart meters only have to add noise to their data and encrypt the results with an efficient stream cipher.
Several public key cryptosystems with additional homomorphic properties have been proposed so far. They allow to perform computation with encrypted data without the knowledge of any secret information. In many applications, the ability to perform decryption, i.e. the knowledge of the secret key, gives a huge power. A classical way to reduce the trust in such a secret owner, and consequently to increase the security, is to share the secret between many entities in such a way that cooperation between them is necessary to decrypt. In this paper, we propose a distributed version of the Paillier cryptosystem presented at Eurocrypt '99. This shared scheme can for example be used in an electronic voting scheme or in a lottery where a random number related to the winning ticket has to be jointly chosen by all participants. A private stream aggregation (PSA) system contributes a user's data to a data aggregator without compromising the user's privacy. The system can begin by determining a private key for a local user in a set of users, wherein the sum of the private keys associated with the set of users and the data aggregator is equal to zero. The system also selects a set of data values associated with the local user. Then, the system encrypts individual data values in the set based in part on the private key to produce a set of encrypted data values, thereby allowing the data aggregator to decrypt an aggregate value across the set of users without decrypting individual data values associated with the set of users, and without interacting with the set of users while decrypting the aggregate value. The system also sends the set of encrypted data values to the data aggregator. We propose the first differentially private aggregation algorithm for distributed time-series data that offers good practical utility without any trusted server. This addresses two important challenges in participatory data-mining applications where (i) individual users collect temporally correlated time-series data (such as location traces, web history, personal health data), and (ii) an untrusted third-party aggregator wishes to run aggregate queries on the data. To ensure differential privacy for time-series data despite the presence of temporal correlation, we propose the Fourier Perturbation Algorithm (FPAk). Standard differential privacy techniques perform poorly for time-series data. To answer n queries, such techniques can result in a noise of Θ(n) to each query answer, making the answers practically useless if n is large. Our FPAk algorithm perturbs the Discrete Fourier Transform of the query answers. For answering n queries, FPAk improves the expected error from Θ(n) to roughly Θ(k) where k is the number of Fourier coefficients that can (approximately) reconstruct all the n query answers. Our experiments show that k To deal with the absence of a trusted central server, we propose the Distributed Laplace Perturbation Algorithm (DLPA) to add noise in a distributed way in order to guarantee differential privacy. To the best of our knowledge, DLPA is the first distributed differentially private algorithm that can scale with a large number of users: DLPA outperforms the only other distributed solution for differential privacy proposed so far, by reducing the computational load per user from O(U) to O(1) where U is the number of users. 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 well-known 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.