4 research outputs found
Secure Social Recommendation based on Secret Sharing
Nowadays, privacy preserving machine learning has been drawing much attention
in both industry and academy. Meanwhile, recommender systems have been
extensively adopted by many commercial platforms (e.g. Amazon) and they are
mainly built based on user-item interactions. Besides, social platforms (e.g.
Facebook) have rich resources of user social information. It is well known that
social information, which is rich on social platforms such as Facebook, are
useful to recommender systems. It is anticipated to combine the social
information with the user-item ratings to improve the overall recommendation
performance. Most existing recommendation models are built based on the
assumptions that the social information are available. However, different
platforms are usually reluctant to (or cannot) share their data due to certain
concerns. In this paper, we first propose a SEcure SOcial RECommendation
(SeSoRec) framework which can (1) collaboratively mine knowledge from social
platform to improve the recommendation performance of the rating platform, and
(2) securely keep the raw data of both platforms. We then propose a Secret
Sharing based Matrix Multiplication (SSMM) protocol to optimize SeSoRec and
prove its correctness and security theoretically. By applying minibatch
gradient descent, SeSoRec has linear time complexities in terms of both
computation and communication. The comprehensive experimental results on three
real-world datasets demonstrate the effectiveness of our proposed SeSoRec and
SSMM.Comment: Accepted by ECAI'2
A Survey on the Security of Pervasive Online Social Networks (POSNs)
Pervasive Online Social Networks (POSNs) are the extensions of Online Social
Networks (OSNs) which facilitate connectivity irrespective of the domain and
properties of users. POSNs have been accumulated with the convergence of a
plethora of social networking platforms with a motivation of bridging their
gap. Over the last decade, OSNs have visually perceived an altogether
tremendous amount of advancement in terms of the number of users as well as
technology enablers. A single OSN is the property of an organization, which
ascertains smooth functioning of its accommodations for providing a quality
experience to their users. However, with POSNs, multiple OSNs have coalesced
through communities, circles, or only properties, which make
service-provisioning tedious and arduous to sustain. Especially, challenges
become rigorous when the focus is on the security perspective of cross-platform
OSNs, which are an integral part of POSNs. Thus, it is of utmost paramountcy to
highlight such a requirement and understand the current situation while
discussing the available state-of-the-art. With the modernization of OSNs and
convergence towards POSNs, it is compulsory to understand the impact and reach
of current solutions for enhancing the security of users as well as associated
services. This survey understands this requisite and fixates on different sets
of studies presented over the last few years and surveys them for their
applicability to POSNs...Comment: 39 Pages, 10 Figure
CryptoRec: Privacy-preserving Recommendation as a Service
Recommender systems rely on large datasets of historical data and entail
serious privacy risks. A server offering Recommendation as a Service to a
client might leak more information than necessary regarding its recommendation
model and dataset. At the same time, the disclosure of the client's preferences
to the server is also a matter of concern. Devising privacy-preserving
protocols using general cryptographic primitives (e.g., secure multi-party
computation or homomorphic encryption), is a typical approach to overcome
privacy concerns, but in conjunction with state-of-the-art recommender systems
often yields far-from-practical solutions.
In this paper, we tackle this problem from the direction of constructing
crypto-friendly machine learning algorithms. In particular, we propose
CryptoRec, a secure two-party computation protocol for Recommendation as a
Service, which encompasses a novel recommender system. This model possesses two
interesting properties: (1) It models user-item interactions in an item-only
latent feature space in which personalized user representations are
automatically captured by an aggregation of pre-learned item features. This
means that a server with a pre-trained model can provide recommendations for a
client whose data is not in its training set. Nevertheless, re-training the
model with the client's data still improves accuracy. (2) It only uses addition
and multiplication operations, making the model straightforwardly compatible
with homomorphic encryption schemes.
We demonstrate the efficiency and accuracy of CryptoRec on three real-world
datasets. CryptoRec allows a server with thousands of items to privately answer
a prediction query within a few seconds on a single PC, while its prediction
accuracy is still competitive with state-of-the-art recommender systems
computing over clear data.Comment: Major Revision: 1. Introduce a new one-iteration re-training process
for the sake of efficiency; 2. Change security level settings; 3, change the
paper title, from "CryptoRec: Secure Recommendations as a Service" to
"CryptoRec: Privacy-preserving Recommendation as a Service
When Services Computing Meets Blockchain: Challenges and Opportunities
Services computing can offer a high-level abstraction to support diverse
applications via encapsulating various computing infrastructures. Though
services computing has greatly boosted the productivity of developers, it is
faced with three main challenges: privacy and security risks, information silo,
and pricing mechanisms and incentives. The recent advances of blockchain bring
opportunities to address the challenges of services computing due to its
build-in encryption as well as digital signature schemes, decentralization
feature, and intrinsic incentive mechanisms. In this paper, we present a survey
to investigate the integration of blockchain with services computing. The
integration of blockchain with services computing mainly exhibits merits in two
aspects: i) blockchain can potentially address key challenges of services
computing and ii) services computing can also promote blockchain development.
In particular, we categorize the current literature of services computing based
on blockchain into five types: services creation, services discovery, services
recommendation, services composition, and services arbitration. Moreover, we
generalize Blockchain as a Service (BaaS) architecture and summarize the
representative BaaS platforms. In addition, we also outline open issues of
blockchain-based services computing and BaaS.Comment: 15 pages, 5 figure