Gaby G. Dagher

DARM : a privacy-preserving approach for distributed association rules mining on horizontally-partitioned data

Extracting association rules helps data owners to unveil hidden patterns from their data for the purpose of analyzing and predicting the behavior of their clients. However, mining association rules in a distributed environment is not a trivial task due to privacy concerns. Data owners are interested in collaborating with each other to mine association rules on a global level; however, they are concerned that sensitive information related to the individuals involved in their database might get compromised during the mining process. In this paper, we formulate and address the problem of answering association rules queries in a distributed environment such that the mining process is confidential and the results are differentially private. We propose a privacy-preserving distributed association rules mining approach, named DARM, where global strong association rules are determined in a confidential way, and the results returned satisfy ε-differential privacy. We conduct our experiments on real-life data, and show that our approach can efficiently answer association rules queries and is scalable with increasing data records.

Subject-based semantic document clustering for digital forensic investigations

Abstract Computers are increasingly used as tools to commit crimes such as unauthorized access (hacking), drug trafficking, and child pornography. The proliferation of crimes involving computers has created a demand for special forensic tools that allow investigators to look for evidence on a suspects computer by analyzing communications and data on the computers storage devices. Motivated by the forensic process at Surete du Quebec ( SQ ), the Quebec provincial police, we propose a new subject-based semantic document clustering model that allows an investigator to cluster documents stored on a suspects computer by grouping them into a set of overlapping clusters, each corresponding to a subject of interest initially defined by the investigator.

Product recommendation system for small online retailers using association rules mining

Recommendation systems in e-commerce have become essential tools to help businesses increase their sales. In this paper, we detail the design of a product recommendation system for small online retailers. Our system is specifically designed to address the needs of retailers with small data pools and limited processing power, and is tested for accuracy, efficiency, and scalability on real life data from a small online retailer.

Fusion: Privacy-Preserving Distributed Protocol for High-Dimensional Data Mashup

In the last decade, several approaches concerning private data release for data mining have been proposed. Data mashup, on the other hand, has recently emerged as a mechanism for integrating data from several data providers. Fusing both techniques to generate mashup data in a distributed environment while providing privacy and utility guarantees on the output involves several challenges. That is, how to ensure that no unnecessary information is leaked to the other parties during the mashup process, how to ensure the mashup data is protected against certain privacy threats, and how to handle the high-dimensional nature of the mashup data while guaranteeing high data utility. In this paper, we present Fusion, a privacy-preserving multi-party protocol for data mashup with guaranteed LKC-privacy for the purpose of data mining. Experiments on real-life data demonstrate that the anonymous mashup data provide better data utility, the approach can handle high dimensional data, and it is scalable with respect to the data size.

BroncoVote: Secure Voting System using Ethereum's Blockchain.

Voting is a fundamental part of democratic systems; it gives individuals in a community the faculty to voice their opinion. In recent years, voter turnout has diminished while concerns regarding integrity, security, and accessibility of current voting systems have escalated. E-voting was introduced to address those concerns; however, it is not cost-effective and still requires full supervision by a central authority. The blockchain is an emerging, decentralized, and distributed technology that promises to enhance different aspects of many industries. Expanding e-voting into blockchain technology could be the solution to alleviate the present concerns in e-voting. In this paper, we propose a blockchain-based voting system, named BroncoVote, that preserves voter privacy and increases accessibility, while keeping the voting system transparent, secure, and cost-effective. BroncoVote implements a university-scaled voting framework that utilizes Ethereum’s blockchain and smart contracts to achieve voter administration and auditable voting records. In addition, BroncoVote utilizes a few cryptographic techniques, including homomorphic encryption, to promote voter privacy. Our implementation was deployed on Ethereum’s Testnet to demonstrate usability, scalability, and efficiency.

SafePath: Differentially-private publishing of passenger trajectories in transportation systems

Abstract In recent years, the collection of spatio-temporal data that captures human movements has increased tremendously due to the advancements in hardware and software systems capable of collecting person-specific data. The bulk of the data collected by these systems has numerous applications, or it can simply be used for general data analysis. Therefore, publishing such big data is greatly beneficial for data recipients. However, in its raw form, the collected data contains sensitive information pertaining to the individuals from which it was collected and must be anonymized before publication. In this paper, we study the problem of privacy-preserving passenger trajectories publishing and propose a solution under the rigorous differential privacy model. Unlike sequential data, which describes sequentiality between data items, handling spatio-temporal data is a challenging task due to the fact that introducing a temporal dimension results in extreme sparseness. Our proposed solution introduces an efficient algorithm, called SafePath, that models trajectories as a noisy prefix tree and publishes ϵ-differentially-private trajectories while minimizing the impact on data utility. Experimental evaluation on real-life transit data in Montreal suggests that SafePath significantly improves efficiency and scalability with respect to large and sparse datasets, while achieving comparable results to existing solutions in terms of the utility of the sanitized data.