Efficient aggregation of encrypted data in wireless sensor networks

By Claude Castelluccia, Einar Mykletun, Gene Tsudik. 2005

619 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

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In Computer Networks and Communications

Citations in the corpus

Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, methodology): “Private data-collection systems [30, 48, 53, 56, 79, 86, 92] 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 priv”
A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, methodology): “Their scheme was inspired by previous work on secure sensor data aggregation in a wireless network [11], [12].”
Efficient Private Statistics with Succinct Sketches (2015, background): “[13] propose a new homomorphic encryption to allow intermediate wireless sensor nodes to aggregate encrypted data gathered from other nodes.”
Towards Fair and Privacy-Preserving Federated Deep Models (2019, methodology): “Therefore, we use simple and provably secure OTP for additively homomorphic encryption that allows efficient aggregation of encrypted data [41], [42].”
Concealed Data Aggregation for Reverse Multicast Traffic in Sensor Networks: Encryption, Key Distribution, and Routing Adaptation (2006, background): “Recently, Castelluccia et al. presented an efficient aggregation of encrypted data in wireless sensor networks [7].”
Secure multiparty aggregation with differential privacy: a comparative study (2013, background): “This problem is typically formulated as a secure multiparty computation problem (SMC), in which parties wish to jointly compute a function of their private data inputs [2,8,9,30,36,37].”
Sensor network security: a survey (2009, methodology): “in [136] describe and evaluate three algorithms: Domingo-Ferrer (DFPH) [137], CMT (they denote it corresponding to the authors initials) - a Key stream based PH [140], Elliptic Curve ElGamal, that were reported to suit to the WSN scenario.”
Internet of things: Vision, applications and research challenges (2012, background): “Some approaches [114–117] suggest to share a key among all sensing nodes and the Sink, the aggregator nodes have not the key because the aggregator nodes handle data without making any encryption/decryption operation.”
Secure hierarchical in-network aggregation in sensor networks (2006, background): “An adversary controlling just a few aggregator nodes could potentially cause the sensor network to return arbitrary results, thus completely subverting the function of the network to the adversary s own purposes.”
A Survey of Homomorphic Encryption for Nonspecialists (2007, background): “be of interest to consider a simple generalization of the onetime pad, where bits are replaced by integers modulo n, as introduced by [68].”
Lightweight Crypto-Assisted Distributed Differential Privacy for Privacy-Preserving Distributed Learning (2020, methodology): “are performed modulo a large integer M [24].”
SDAP: a secure hop-by-Hop data aggregation protocol for sensor networks (2006, background): “Recently, many data aggregation protocols [Estrin et al. 1999; Intanagonwiwat et al. 2002, 2000; Krishnamachari et al. 2002; Madden et al. 2002; Castelluccia et al. 2005; Chen et al. 2005] have been proposed to eliminate the data redundancy in sensor data of the network, hence reducing the communica”
A Survey on Wireless Sensor Network Security (2009, methodology): “Two ciphertext-based secure data aggregation schemes have been proposed in [147] and [148].”
PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks (2007, background): “additive aggregation functions is not too restrictive, since many other aggregation functions, including average, count, variance, standard deviation and any other moment of the measured data, can be reduced to the additive aggregation function sum [16].”