https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

Data Mining - Concepts and Techniques.

Matrix Factorization Techniques for Recommender Systems

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

https://www.alaakhamis.org/teaching/MCT200/reading/Information%20Fusion.pdf

Multisensor data fusion: A review of the state-of-the-art

Data aggregation is a widely used technique in wireless sensor networks. The security issues, data confidentiality and integrity, in data aggregation become vital when the sensor network is deployed in a hostile environment. There has been many related work proposed to address these security issues. In this paper we survey these work and classify them into two cases: hop-by-hop encrypted data aggregation and end-to-end encrypted data aggregation. We also propose two general frameworks for the two cases respectively. The framework for end-to-end encrypted data aggregation has higher computation cost on the sensor nodes, but achieves stronger security, in comparison with the framework for hop-by-hop encrypted data aggregation.

/pdf/secure-data-aggregation-in-wireless-sensor-networks-a-survey-rnq456h8pl.pdf

Secure Data Aggregation in Wireless Sensor Networks: A Survey

When datasets are distributed on different sources, finding out matched data while preserving the privacy of the datasets is a widely required task. In this paper, we address two matching problems against the private datasets on N (N≥2) parties. The first one is the Privacy Preserving Set Intersection (PPSI) problem, in which each party wants to learn the intersection of the N private datasets. The second one is the Privacy Preserving Set Matching (PPSM) problem, in which each party wants to learn whether its elements can be matched in any private set of the other parties. For the two problems we propose efficient protocols based on a threshold cryptosystem which is additive homomorphic. In a comparison with the related work in [18], the computation and communication costs of our PPSI protocol decrease by 81% and 17% respectively, and the computation and communication costs of our PPSM protocol decrease by 80% and 50% respectively. In practical utilities both of our protocols save computation time and communication bandwidth.

/pdf/efficient-protocols-for-privacy-preserving-matching-against-1r8kpx0foh.pdf

Efficient protocols for privacy preserving matching against distributed datasets

Many applications require performing set operations without publishing individual datesets. In this article, we address this problem for five fundamental set operations including set intersection, cardinality of set intersection, element reduction, overthreshold set-union, and subset relation. Our protocols are obtained in the universally composable security framework, in the assumption of the probabilistic polynomial time bounded adversary, which actively controls a fixed set of t parties and the assumption of an authenticated broadcast channel. Our constructions utilize building blocks of nonmalleable NonInteractive Zero-Knowledge (NIZK) arguments, which are based on a (t + 1,N)-threshold version (N is the number of parties in the protocol) of the boneh-goh-nissim (BGN) cryptosystem whose underlying group supports bilinear maps, in the assumption that the public key and shares of the secret key have been generated by a trusted dealer. The previous studies were all based on the stand-alone model with the same assumptions on the adversary, broadcast channel, and key generation. For the first four operations, we propose protocols that improve the previously known results by an O(N) factor in the computation and communication complexities. For the subset relation, our protocol is the first one secure against the active adversary. Our constructions of NIZK have independent interest in that, though also mentioned as building blocks, the previous work did not illustrate how to construct them. We construct these NIZK with an additional nonmalleable property, the same complexity as claimed in the previous work, and also an improvement on the communication complexity.

Efficient and secure protocols for privacy-preserving set operations

When datasets are distributed on different sources, finding out matched data while preserving the privacy of the datasets is a widely required task. In this paper, we address two matching problems against the private datasets on N (N > 2) parties. The first one is the Privacy Preserving Set Intersection (PPSI) problem, in which each party wants to learn the intersection of the N private datasets. The second one is the Privacy Preserving Set Matching (PPSM) problem, in which each party wants to learn whether its elements can be matched in any private set of the other parties. For the two problems we propose efficient protocols based on a threshold cryptosystem which is additive homomorphic. In a comparison with the related work in [18], the computation and communication costs of our PPSI protocol decrease by 81% and 17% respectively, and the computation and communication costs of our PPSM protocol decrease by 80% and 50% respectively. In practical utilities both of our protocols save computation time and communication bandwidth.

Yingpeng Sang

Papers

Secure Data Aggregation in Wireless Sensor Networks: A Survey

Efficient protocols for privacy preserving matching against distributed datasets

Efficient and secure protocols for privacy-preserving set operations

Efficient protocols for privacy preserving matching against distributed datasets

Privacy-preserving data publishing for multiple numerical sensitive attributes