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Research My research goal is to develop a trusted middleware which would facilitate privacy preserving collaborative applications in online business and social communities. To achieve this goal, I have mainly focused on efficient and practical secure multiparty computation protocols, high performance data analysis techniques, privacy sensitive user modeling and context-aware recommendation systems.
Under
Review
TALKS/PRESENTATIONS
Below are links to
research projects that I’ve been involved in. (Advisor: Prof. Ashfaq Khokhar)
›
Phoenix:
Privacy Preserving Biclustering on Horizontally Partitioned Data amid
Malicious Adversaries. Emerging
business models require organizations to collaborate with each other. This
collaboration is usually in the form of distributed clustering to find
optimal customer targets for effective marketing. This process is hampered by
two problems (1) Inability of traditional clustering algorithm in finding
local (subspace) patterns in distributed data and (2) Privacy policies of
individual organizations limiting the process of information sharing. In this
paper, we propose an efficient privacy preserving Biclustering algorithm on
horizontally partitioned data, referred to as ›
SPHier: SPHier: Scalable Parallel Biclustering
Using Weighted Bigraph Crossing Minimization: Biclustering is used for
discovering correlations among subsets of attributes with subsets of transactions
in a transaction database. It has an extensive set of applications ranging
from Gene co-regulation analysis, document-keyword clustering and
collaborative filtering for online recommendation systems. In this paper, we
propose optimal biclustering problem as maximal crossing number reduction in
a weighted bipartite graph. Based on the problem formulation, we then present
SPHier, a novel parallel biclustering algorithm based on weighted bigraph
crossing minimization problem. Crossing minimization has been extensively
used in Graph Drawing and VLSI Circuit Layouts for reducing wire congestion
while its application to scalable parallel biclustering problem, to the best
of our knowledge, is being investigated for the first time in this paper. We
show that crossing minimization approach provides a simple and intuitive
method to identify bi-clusters. Moreover, it is much easier to parallelize
with excellent speedup characteristics. We have validated SPHier on synthetic
and biological data sets. We show performance results on an › TRIUMF: TRIUMF, the Trusted Middleware for Fault-tolerant secure collaborative computing, is aimed at enabling privacy preserving collaborative processes across administrative domains. The middleware is built around a Services Oriented Architecture comprising of an ensemble of services. The membership services are responsible for secure and authenticated entry into the collaboration process. Data Request Processing Services are responsible for deciding the level of privacy required along with the selection of appropriate aggregation functions and/or proper Secure Multiparty Protocols. Secure Data Access Services are responsible for providing efficient access to private and non-private data sources. Privacy Preserving Data Processing services provide mechanisms whereby secure sharing of data, information or knowledge is made possible. The High Availability services provide mechanisms to ensure fault tolerant execution of applications running on TRIUMF. TRIUMF is being developed on top of JXTA, which provides an open source P2P networking infrastructure. 1. Secure Aggregation In Large Scale Overlay Networks: Overlay networks have been very useful in solving large scale data dissemination problems. In this paper, we consider the case of data gathering which is the inverse of dissemination problem. In particular, we focus on a scenario where an organization or a constellation of organizations is interested in gathering data from large number of nodes spread across the administrative boundaries. Providing individual nodes with full assurance that the privacy of their data won’t be compromised is a critical problem in achieving the true benefits of this collaborative process. We provide a novel solution to the problem by employing a homomorphic cryptosystem which allows processing of encrypted data without revealing anything about the underlying private(plain text) data. We also make the cryptosystem ”threshold” so that no single node is able to decrypt the aggregate results. We make use of a hierarchical communication protocol as opposed to a gossip protocol based on nature of the application scenarios that we are addressing. The proposed solution provides excellent scale-up properties while preserving privacy and secrecy of the data even among malicious adversarial constraints. [Globecom06 Paper-PDF] 2. SAMcast - A Scalable, Secure and Authenticated Multicast Protocol for Large Scale P2P Networks: Overlay networks have shown tremendous potential in solving large scale data dissemination problem by employing peer-to-peer communication protocols. These networks, however, have mostly been used for illegal dissemination of copyrighted material. This paper is aimed at investigating an incentive driven approach to encourage users to actively participate in overlay activities. The users are also discouraged from indulging in illegal distribution of copyrighted material by employing an efficient public key based broadcast encryption scheme along with a deterministic traitor tracing mechanism. We note that public key based broadcast encryption schemes require some mechanism by which a peer can verify the integrity of contents downloaded from other peers. SAMcast is the first protocol, to the best of our knowledge, which provides an efficient integrity verification mechanism along with public key based broadcast encryption. Our experiment results show that the proposed broadcast encryption scheme is highly scalable and the integrity verification is extremely efficient both in terms of computation and communication. [PDF] 3. Privacy Preserving Collaborative Filtering: 1. Pervasive Computing Environments: Collaborative Filtering (CF) is a method to perform Automated Recommendations based upon the assumption that users who had similar interests in past, will have similar interests in future too. Current server based collaborative filtering algorithms pose a serious threat to user privacy. In this paper, we present a novel architecture for privacy preserving collaborative Filtering on a large scale overlay network. The proposed privacy preserving collaborative filtering employs a crossing minimization based efficient biclustering algorithm and a threshold homomorphic cryptosystem for privacy preserving secure multiparty computations. The proposed algorithm is fully implemented and evaluated on a simulated distributed network.[PDF] 2.
For Web Portals and Web 2.0 Applications:
Collaborative Filtering (CF) is a method to perform Automated Recommendations
based upon the assumption that users who had similar interests in past, will
have similar interests in future too. Popularity of e-commerce portals such
as Amazon and Ebay and Web 2.0 applications such as YouTube and Flickr is
resulting in private user data being stored in central servers. This has
given rise to a number of privacy concerns[\ref{cranor}] which are effecting
business of these services[See Cyber
Dialogue]. In this paper, we present a novel architecture for privacy
preserving collaborative Filtering for these services. The proposed
architecture attempts to restore user trust in these services by essentially
introducing a notion of 'Distributed Trust' where instead of trusting a
single server, a coalition of servers is trusted. The proposed privacy
preserving collaborative filtering employs a crossing minimization based efficient
biclustering algorithm and a threshold homomorphic cryptosystem for privacy
preserving secure multiparty computations eliminating the requirement of a
single trusted server. The proposed algorithm is fully implemented and
evaluated with encouraging results. [PDF] Previous Projects 1- UNAHA:
Unified Navigation Architecture for Hypertext Applications (BS Final Year
(Senior) Project) Advisor: Prof. Shahid H Bokhari. |