Privacy-Preserving Data Aggregation in Peer-to-Peer Network

Privacy-Preserving Data Aggregation in Peer-to-Peer Network: A Multiparty Computation Approach

Prahesa Kusuma Setia, Prahesa (TU Delft Electrical Engineering, Mathematics and Computer Science)

Erkin, Zekeriya (mentor)
Epema, Dick (graduation committee)
Hildebrandt, Klaus (graduation committee)

Delft University of Technology

Computer Science

2017-11-29

Current interconnected society provides us with numerous devices communicating with one another. Exchange of data thus become an integral part in our live. Data become valuable commodity in today's setting because of their usage by individual and other interested parties. Several parties may be interested in computing a function over their data while still want to keep the information on their own data private.

Prior research on computing function in privacy preserving way in the domain of smart power-grid, e-metering system, wireless sensor network, and smart phone sensing generally focus on their own application and assume a total control and the static structure of the network. Moreover, a new paradigm in the field of decentralized power-grid requires privacy preserving solution to be applicable without existence of central authority. We propose two privacy preserving data aggregation protocols in peer-to-peer network scenario where there is such central authority involved. The first protocol utilizes additive homomorphism properties of Pailier scheme and the second protocol utilizes secret sharing. Both of the protocol achieve privacy-preserving requirement of some nodes in the network, as opposed to all nodes, that are included in the aggregation set by a hop count parameter from the initiating node. This way, both of the protocols require no information of overall network structure and privacy-preserving data aggregation is achieved by being able to communicate with direct neighbors of each node in the network only.

cryptography
Privacy
Network

http://resolver.tudelft.nl/uuid:d512b49d-665d-461c-8317-db7da3ed8eec

Student theses

master thesis

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