8 research outputs found
Reduce to the Max: A Simple Approach for Massive-Scale Privacy-Preserving Collaborative Network Measurements (Extended Version)
Privacy-preserving techniques for distributed computation have been proposed
recently as a promising framework in collaborative inter-domain network
monitoring. Several different approaches exist to solve such class of problems,
e.g., Homomorphic Encryption (HE) and Secure Multiparty Computation (SMC) based
on Shamir's Secret Sharing algorithm (SSS). Such techniques are complete from a
computation-theoretic perspective: given a set of private inputs, it is
possible to perform arbitrary computation tasks without revealing any of the
intermediate results. In fact, HE and SSS can operate also on secret inputs
and/or provide secret outputs. However, they are computationally expensive and
do not scale well in the number of players and/or in the rate of computation
tasks. In this paper we advocate the use of "elementary" (as opposite to
"complete") Secure Multiparty Computation (E-SMC) procedures for traffic
monitoring. E-SMC supports only simple computations with private input and
public output, i.e., it can not handle secret input nor secret (intermediate)
output. Such a simplification brings a dramatic reduction in complexity and
enables massive-scale implementation with acceptable delay and overhead.
Notwithstanding its simplicity, we claim that an E-SMC scheme is sufficient to
perform a great variety of computation tasks of practical relevance to
collaborative network monitoring, including, e.g., anonymous publishing and set
operations. This is achieved by combining a E-SMC scheme with data structures
like Bloom Filters and bitmap strings.Comment: This is an extended version of the paper presented at the Third
International Workshop on Traffic Monitoring and Analysis (TMA'11), Vienna,
27 April 201
Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners
The k-nearest neighbors (k-NN) algorithm is a popular and effective
classification algorithm. Due to its large storage and computational
requirements, it is suitable for cloud outsourcing. However, k-NN is often run
on sensitive data such as medical records, user images, or personal
information. It is important to protect the privacy of data in an outsourced
k-NN system.
Prior works have all assumed the data owners (who submit data to the
outsourced k-NN system) are a single trusted party. However, we observe that in
many practical scenarios, there may be multiple mutually distrusting data
owners. In this work, we present the first framing and exploration of privacy
preservation in an outsourced k-NN system with multiple data owners. We
consider the various threat models introduced by this modification. We discover
that under a particularly practical threat model that covers numerous
scenarios, there exists a set of adaptive attacks that breach the data privacy
of any exact k-NN system. The vulnerability is a result of the mathematical
properties of k-NN and its output. Thus, we propose a privacy-preserving
alternative system supporting kernel density estimation using a Gaussian
kernel, a classification algorithm from the same family as k-NN. In many
applications, this similar algorithm serves as a good substitute for k-NN. We
additionally investigate solutions for other threat models, often through
extensions on prior single data owner systems
Private Top-k Aggregation Protocols
In this paper, we revisit the private top-κ data aggregation problem. First we formally define the problem’s security requirements as both data and user privacy goals. To achieve both goals, and to strike a balance between efficiency and functionality, we devise a novel cryptographic construction that comes in two schemes; a fully decentralized simple construction and its practical and semi-decentralized variant. Both schemes are provably secure in the semi-honest model. We analyze the computational and communi- cation complexities of our construction, and show that it is much more efficient than the existing protocols in the literature
Prism: Private Set Intersection and Union with Aggregation over Multi-Owner Outsourced Data
This paper proposes Prism, Private Verifiable Set Computation over Multi-Owner Outsourced Databases, a secret sharing based approach to compute private set operations (i.e., intersection and union), as well as aggregates over outsourced databases belonging to multiple owners. Prism enables data owners to pre-load the data onto non-colluding servers and exploits the additive and multiplicative properties of secret-shares to compute the above-listed operations in (at most) two rounds of communication between the servers (storing the secret-shares) and the querier, resulting in a very efficient implementation. Also, Prism does not require communication among the servers and supports result verification techniques for each operation to detect malicious adversaries. Experimental results show that Prism scales both in terms of the number of data owners and database sizes, to which prior approaches do not scale