text: This paper presents Prio, a privacy-preserving system for the collection of aggregate statistics. Each Prio client holds a private data value (e.g., its current location), and a small set of servers compute statistical functions over the values of all clients (e.g., the most popular location). As long as at least one server is honest, the Prio servers learn nearly nothing about the clients' private data, except what they can infer from the aggregate statistics that the system computes. To protect functionality in the face of faulty or malicious clients, Prio uses secret-shared non-interactive proofs (SNIPs), a new cryptographic technique that yields a hundred-fold performance improvement over conventional zero-knowledge approaches. Prio extends classic private aggregation techniques to enable the collection of a large class of useful statistics. For example, Prio can perform a least-squares regression on high-dimensional client-provided data without ever seeing the data in the clear.
  @cite_78: 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.
  @cite_8: The widespread deployment of smart meters for the modernisation of the electricity distribution network, but also for gas and water consumption, has been associated with privacy concerns due to the potentially large number of measurements that reflect the consumers behaviour. In this paper, we present protocols that can be used to privately compute aggregate meter measurements over defined sets of meters, allowing for fraud and leakage detection as well as network management and further statistical processing of meter measurements, without revealing any additional information about the individual meter readings. Thus, most of the benefits of the Smart Grid can be achieved without revealing individual data. The feasibility of the protocols has been demonstrated with an implementation on current smart meters.
  @cite_60: In this paper we introduce a framework for privacy-preserving distributed computation that is practical for many real-world applications. The framework is called Peers for Privacy (P4P) and features a novel heterogeneous architecture and a number of efficient tools for performing private computation and ensuring security at large scale. It maintains the following properties: (1) Provably strong privacy; (2) Adequate efficiency at reasonably large scale; and (3) Robustness against realistic adversaries. The framework gains its practicality by decomposing data mining algorithms into a sequence of vector addition steps that can be privately evaluated using a new verifiable secret sharing (VSS) scheme over small field (e.g., 32 or 64 bits), which has the same cost as regular, non-private arithmetic. This paradigm supports a large number of statistical learning algorithms including SVD, PCA, k-means, ID3, EM-based machine learning algorithms, etc., and all algorithms in the statistical query model [36]. As a concrete example, we show how singular value decomposition (SVD), which is an extremely useful algorithm and the core of many data mining tasks, can be done efficiently with privacy in P4P. Using real-world data and actual implementation we demonstrate that P4P is orders of magnitude faster than existing solutions.
  @cite_95: 
  @cite_72: 
  @cite_63: In addition to their common use for private online communication, anonymous communication networks can also be used to circumvent censorship. However, it is difficult to determine the extent to which they are actually used for this purpose without violating the privacy of the networks' users. Knowing this extent can be useful to designers and researchers who would like to improve the performance and privacy properties of the network. To address this issue, we propose a statistical data collection system, PrivEx, for collecting egress traffic statistics from anonymous communication networks in a secure and privacy-preserving manner. Our solution is based on distributed differential privacy and secure multiparty computation; it preserves the security and privacy properties of anonymous communication networks, even in the face of adversaries that can compromise data collection nodes or coerce operators to reveal cryptographic secrets and keys.
  @cite_80: There is a growing body of research on mechanisms for preserving online user privacy while still allowing aggregate queries over private user data. A common approach is to store user data at users' devices, and to query the data in such a way that a differentially private noisy result is produced without exposing individual user data to any system component. A particular challenge is to design a system that scales well while limiting how much the malicious users can distort the result. This paper presents SplitX, a high-performance analytics system for making differentially private queries over distributed user data. SplitX is typically two to three orders of magnitude more efficient in bandwidth, and from three to five orders of magnitude more efficient in computation than previous comparable systems, while operating under a similar trust model. SplitX accomplishes this performance by replacing public-key operations with exclusive-or operations. This paper presents the design of SplitX, analyzes its security and performance, and describes its implementation and deployment across 416 users.
  @cite_73: 
summary: Private data-collection systems @cite_63 @cite_78 @cite_73 @cite_95 @cite_8 @cite_60 @cite_72 that use secret-sharing based methods to compute sums over private user data typically (a) provide no robustness guarantees in the face of malicious clients, (b) use expensive NIZKs to prevent client misbehavior, or (c) fail to defend privacy against actively malicious servers @cite_80 .