710 research outputs found
Privacy-preserving outsourced calculation on floating point numbers
National Research Foundation (NRF) Singapor
A privacy-preserving fuzzy interest matching protocol for friends finding in social networks
Nowadays, it is very popular to make friends, share photographs, and exchange news throughout social networks. Social networks widely expand the area of people’s social connections and make communication much smoother than ever before. In a social network, there are many social groups established based on common interests among persons, such as learning group, family group, and reading group. People often describe their profiles when registering as a user in a social network. Then social networks can organize these users into groups of friends according to their profiles. However, an important issue must be considered, namely many users’ sensitive profiles could have been leaked out during this process. Therefore, it is reasonable to design a privacy-preserving friends-finding protocol in social network. Toward this goal, we design a fuzzy interest matching protocol based on private set intersection. Concretely, two candidate users can first organize their profiles into sets, then use Bloom filters to generate new data structures, and finally find the intersection sets to decide whether being friends or not in the social network. The protocol is shown to be secure in the malicious model and can be useful for practical purposes.Peer ReviewedPostprint (author's final draft
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure
function evaluation (SFE) which enables two parties to jointly compute a
function without disclosing their private inputs. Chameleon combines the best
aspects of generic SFE protocols with the ones that are based upon additive
secret sharing. In particular, the framework performs linear operations in the
ring using additively secret shared values and nonlinear
operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson
protocol. Chameleon departs from the common assumption of additive or linear
secret sharing models where three or more parties need to communicate in the
online phase: the framework allows two parties with private inputs to
communicate in the online phase under the assumption of a third node generating
correlated randomness in an offline phase. Almost all of the heavy
cryptographic operations are precomputed in an offline phase which
substantially reduces the communication overhead. Chameleon is both scalable
and significantly more efficient than the ABY framework (NDSS'15) it is based
on. Our framework supports signed fixed-point numbers. In particular,
Chameleon's vector dot product of signed fixed-point numbers improves the
efficiency of mining and classification of encrypted data for algorithms based
upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer
convolutional deep neural network shows 133x and 4.2x faster executions than
Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively
Dodrant-Homomorphic Encryption for Cloud Databases using Table Lookup
Users of large commercial databases increasingly want to outsource their database operations to a cloud service providers, but guaranteeing the privacy of data in an outsourced database has become the major obstacle to this move. Encrypting all data solves the privacy issue, but makes many operations on the data impossible in the cloud, unless the service provider has the capacity to decrypt data temporarily. Homomorphic encryption would solve this issue, but despite great and on-going progress, it is still far from being operationally feasible. In 2015, we presented what we now call dodrant-homomorphic encryption, a method that encrypts numeric values deterministically using the additively homomorphic Paillier encryption and uses table lookup in order to implement multiplications. We discuss here the security implications of determinism and discuss options to avoid these pitfalls
Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data
The k-means clustering is one of the most popular clustering algorithms in
data mining. Recently a lot of research has been concentrated on the algorithm
when the dataset is divided into multiple parties or when the dataset is too
large to be handled by the data owner. In the latter case, usually some servers
are hired to perform the task of clustering. The dataset is divided by the data
owner among the servers who together perform the k-means and return the cluster
labels to the owner. The major challenge in this method is to prevent the
servers from gaining substantial information about the actual data of the
owner. Several algorithms have been designed in the past that provide
cryptographic solutions to perform privacy preserving k-means. We provide a new
method to perform k-means over a large set using multiple servers. Our
technique avoids heavy cryptographic computations and instead we use a simple
randomization technique to preserve the privacy of the data. The k-means
computed has exactly the same efficiency and accuracy as the k-means computed
over the original dataset without any randomization. We argue that our
algorithm is secure against honest but curious and passive adversary.Comment: 19 pages, 4 tables. International Conference on Information Systems
Security. Springer, Cham, 201
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