Mining Frequent Graph Patterns with Differential Privacy

Discovering frequent graph patterns in a graph database offers valuable information in a variety of applications. However, if the graph dataset contains sensitive data of individuals such as mobile phone-call graphs and web-click graphs, releasing discovered frequent patterns may present a threat to the privacy of individuals. {\em Differential privacy} has recently emerged as the {\em de facto} standard for private data analysis due to its provable privacy guarantee. In this paper we propose the first differentially private algorithm for mining frequent graph patterns. We first show that previous techniques on differentially private discovery of frequent {\em itemsets} cannot apply in mining frequent graph patterns due to the inherent complexity of handling structural information in graphs. We then address this challenge by proposing a Markov Chain Monte Carlo (MCMC) sampling based algorithm. Unlike previous work on frequent itemset mining, our techniques do not rely on the output of a non-private mining algorithm. Instead, we observe that both frequent graph pattern mining and the guarantee of differential privacy can be unified into an MCMC sampling framework. In addition, we establish the privacy and utility guarantee of our algorithm and propose an efficient neighboring pattern counting technique as well. Experimental results show that the proposed algorithm is able to output frequent patterns with good precision

Anonymous subject identification and privacy information management in video surveillance

The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework

Knowing Your Population: Privacy-Sensitive Mining of Massive Data

Location and mobility patterns of individuals are important to environmental planning, societal resilience, public health, and a host of commercial applications. Mining telecommunication traffic and transactions data for such purposes is controversial, in particular raising issues of privacy. However, our hypothesis is that privacy-sensitive uses are possible and often beneficial enough to warrant considerable research and development efforts. Our work contends that peoples behavior can yield patterns of both significant commercial, and research, value. For such purposes, methods and algorithms for mining telecommunication data to extract commonly used routes and locations, articulated through time-geographical constructs, are described in a case study within the area of transportation planning and analysis. From the outset, these were designed to balance the privacy of subscribers and the added value of mobility patterns derived from their mobile communication traffic and transactions data. Our work directly contrasts the current, commonly held notion that value can only be added to services by directly monitoring the behavior of individuals, such as in current attempts at location-based services. We position our work within relevant legal frameworks for privacy and data protection, and show that our methods comply with such requirements and also follow best-practice

Surveillance and the Self: Understanding Privacy and Identity in Digital Environments

The widespread use of internet enabled devices among contemporary US adults has given rise to a series of questions about issues of identity, privacy and group behaviors. The increasing use of algorithmic systems in social media and the attendant privacy concerns among users may also contribute to increased levels of strategic management of identity among users. In order to contribute to this discussion, this project examines perceptions and practices of privacy and self-representation in digital spaces among college age adults 18-24. This project utilizes semi-structured interview data collected with college students in the Eastern United States and focuses on both behavioral and attitudinal patterns. I specifically consider the impact of strategic interventions of corporate media platforms to collect, distribute, manage and utilize individual level data on participants\u27 information consumption, individual identity representation and group affiliation. Preliminary data suggests that participants engage partial and strategic representations of self across diverse media platforms. Patterns of self-representation are shaped by a wide variety of factors including in-group online community norms, perceptions of visibility and privacy, algorithmic distributions of information and individual perceptions of technology. Furthermore, online identity, while partial and strategically created, has the potential to impact self-identity and group affiliation in a diverse set of offline and online contexts

Analysing the Design of Privacy-Preserving Data-Sharing Architecture

Privacy has become an essential software quality to consider in a software system. Privacy practices should be adopted from the early stages of the system design to safeguard personal data from privacy violations. Privacy patterns are proposed in industry and academia as reusable design solutions to address different privacy issues. However, the diverse types and granularity of the patterns lead to difficulty for the practitioner to select and adopt them in the architecture. First, the fragmented information about the system actors in the patterns does not align with the regulatory entities and interactions between them. Second, these privacy patterns lack architectural perspectives that could help weave patterns into concrete software designs. Third, the consequences of applying the patterns have not covered the impacts on software quality attributes. This thesis aims to provide guidance to software architects and practitioners for considering and applying privacy patterns in their design, by adding new perspectives to the existing patterns. First, the research provides an analysis of the relationships between regulatory entities and their responsibility in adopting the patterns in a software design. Then, the research reports studies that were conducted using architectural-level modelling-based approaches, to analyse the architectural views of privacy patterns. The analyses aim to improve understanding of how privacy patterns are applied in software designs and how such a design affects software quality attributes, including privacy, performance, and modifiability. Finally, in an effort to harmonise and unite the extended view of privacy patterns that have a close relation to system architecture, this research proposes an enhanced pattern catalogue and a systematic privacy-by-design (PbD) pattern-selection model that aims to aid and guide software architects in pattern selection during software design. The enhanced pattern catalogue offers consolidated information on the extended view of privacy patterns. The selection model provides a structured way for the practitioner to know when and how to use the pattern catalogue in the system-design process. Two industry case studies are used to evaluate the proposed pattern catalogue and selection model. The findings demonstrate how the proposed frameworks are applicable to different types of data-sharing software systems and their usability in supporting pattern selection decisions in the privacy design

A Developer-Friendly Library for Smart Home IoT Privacy-Preserving Traffic Obfuscation

The number and variety of Internet-connected devices have grown enormously in the past few years, presenting new challenges to security and privacy. Research has shown that network adversaries can use traffic rate metadata from consumer IoT devices to infer sensitive user activities. Shaping traffic flows to fit distributions independent of user activities can protect privacy, but this approach has seen little adoption due to required developer effort and overhead bandwidth costs. Here, we present a Python library for IoT developers to easily integrate privacy-preserving traffic shaping into their products. The library replaces standard networking functions with versions that automatically obfuscate device traffic patterns through a combination of payload padding, fragmentation, and randomized cover traffic. Our library successfully preserves user privacy and requires approximately 4 KB/s overhead bandwidth for IoT devices with low send rates or high latency tolerances. This overhead is reasonable given normal Internet speeds in American homes and is an improvement on the bandwidth requirements of existing solutions.Comment: 6 pages, 6 figure