scholarly journals A User-Centric Mechanism for Sequentially Releasing Graph Datasets under Blowfish Privacy

2021 ◽  
Vol 21 (1) ◽  
pp. 1-25
Author(s):  
Elie Chicha ◽  
Bechara Al Bouna ◽  
Mohamed Nassar ◽  
Richard Chbeir ◽  
Ramzi A. Haraty ◽  
...  
Keyword(s):  
Discrete Time ◽  
Differential Privacy ◽  
State Of The Art ◽  
Graph Model ◽  
Privacy Preserving ◽  
Direct Application ◽  
Time Dimension ◽  
User Centric ◽  
Correlation Attacks ◽  
Do So

In this article, we present a privacy-preserving technique for user-centric multi-release graphs. Our technique consists of sequentially releasing anonymized versions of these graphs under Blowfish Privacy. To do so, we introduce a graph model that is augmented with a time dimension and sampled at discrete time steps. We show that the direct application of state-of-the-art privacy-preserving Differential Private techniques is weak against background knowledge attacker models. We present different scenarios where randomizing separate releases independently is vulnerable to correlation attacks. Our method is inspired by Differential Privacy (DP) and its extension Blowfish Privacy (BP). To validate it, we show its effectiveness as well as its utility by experimental simulations.

scholarly journals An Analysis of Differential Privacy Research in Location and Trajectory Data

2020 ◽  
Author(s):  
Fatima Zahra Errounda ◽  
Yan Liu
Keyword(s):  
Location Privacy ◽  
Differential Privacy ◽  
State Of The Art ◽  
Original Data ◽  
Privacy Preserving ◽  
The State ◽  
Trajectory Data ◽  
Powerful Technique ◽  
Location Data ◽  
Single User

Abstract Location and trajectory data are routinely collected to generate valuable knowledge about users' pattern behavior. However, releasing location data may jeopardize the privacy of the involved individuals. Differential privacy is a powerful technique that prevents an adversary from inferring the presence or absence of an individual in the original data solely based on the observed data. The first challenge in applying differential privacy in location is that a it usually involves a single user. This shifts the adversary's target to the user's locations instead of presence or absence in the original data. The second challenge is that the inherent correlation between location data, due to people's movement regularity and predictability, gives the adversary an advantage in inferring information about individuals. In this paper, we review the differentially private approaches to tackle these challenges. Our goal is to help newcomers to the field to better understand the state-of-the art by providing a research map that highlights the different challenges in designing differentially private frameworks that tackle the characteristics of location data. We find that in protecting an individual's location privacy, the attention of differential privacy mechanisms shifts to preventing the adversary from inferring the original location based on the observed one. Moreover, we find that the privacy-preserving mechanisms make use of the predictability and regularity of users' movements to design and protect the users' privacy in trajectory data. Finally, we explore how well the presented frameworks succeed in protecting users' locations and trajectories against well-known privacy attacks.

scholarly journals Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing

2019 ◽  
Vol 2019 (3) ◽  
pp. 233-254 ◽  
Author(s):  
Changchang Liu ◽  
Xi He ◽  
Thee Chanyaswad ◽  
Shiqiang Wang ◽  
Prateek Mittal
Keyword(s):  
Hypothesis Testing ◽  
Gold Standard ◽  
Prior Distribution ◽  
Differential Privacy ◽  
State Of The Art ◽  
Auxiliary Information ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Statistical Tool ◽  
Practical Guidelines

Abstract Over the last decade, differential privacy (DP) has emerged as the gold standard of a rigorous and provable privacy framework. However, there are very few practical guidelines on how to apply differential privacy in practice, and a key challenge is how to set an appropriate value for the privacy parameter ɛ. In this work, we employ a statistical tool called hypothesis testing for discovering useful and interpretable guidelines for the state-of-the-art privacy-preserving frameworks. We formalize and implement hypothesis testing in terms of an adversary’s capability to infer mutually exclusive sensitive information about the input data (such as whether an individual has participated or not) from the output of the privacy-preserving mechanism. We quantify the success of the hypothesis testing using the precision- recall-relation, which provides an interpretable and natural guideline for practitioners and researchers on selecting ɛ. Our key results include a quantitative analysis of how hypothesis testing can guide the choice of the privacy parameter ɛ in an interpretable manner for a differentially private mechanism and its variants. Importantly, our findings show that an adversary’s auxiliary information - in the form of prior distribution of the database and correlation across records and time - indeed influences the proper choice of ɛ. Finally, we also show how the perspective of hypothesis testing can provide useful insights on the relationships among a broad range of privacy frameworks including differential privacy, Pufferfish privacy, Blowfish privacy, dependent differential privacy, inferential privacy, membership privacy and mutual-information based differential privacy.

Privacy-Preserving Hybrid K-Means

2018 ◽  
Vol 14 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Zhiqiang Gao ◽  
Yixiao Sun ◽  
Xiaolong Cui ◽  
Yutao Wang ◽  
Yanyu Duan ◽  
...  
Keyword(s):  
Differential Privacy ◽  
State Of The Art ◽  
Privacy Preserving ◽  
Local Optimum ◽  
Massive Data ◽  
Data Sets ◽  
Second Stage ◽  
Private Data ◽  
Privacy Budget ◽  
Selection Of

This article describes how the most widely used clustering, k-means, is prone to fall into a local optimum. Notably, traditional clustering approaches are directly performed on private data and fail to cope with malicious attacks in massive data mining tasks against attackers' arbitrary background knowledge. It would result in violation of individuals' privacy, as well as leaks through system resources and clustering outputs. To address these issues, the authors propose an efficient privacy-preserving hybrid k-means under Spark. In the first stage, particle swarm optimization is executed in resilient distributed datasets to initiate the selection of clustering centroids in the k-means on Spark. In the second stage, k-means is executed on the condition that a privacy budget is set as ε/2t with Laplace noise added in each round of iterations. Extensive experimentation on public UCI data sets show that on the premise of guaranteeing utility of privacy data and scalability, their approach outperforms the state-of-the-art varieties of k-means by utilizing swarm intelligence and rigorous paradigms of differential privacy.

Structural Analysis of Product and Computer-Aided Assembly Planning in AssemBL Software Package

2018 ◽  
pp. 11-33
Author(s):  
A. N. Bozhko
Keyword(s):  
Computer Aided Design ◽  
State Of The Art ◽  
Graph Model ◽  
Design System ◽  
Assembly Planning ◽  
Complex Products ◽  
Assembly Sequences ◽  
Computer Aided ◽  
Cad Cam ◽  
Aided Design

Computer-aided design of assembly processes (Computer aided assembly planning, CAAP) of complex products is an important and urgent problem of state-of-the-art information technologies. Intensive research on CAAP has been underway since the 1980s. Meanwhile, specialized design systems were created to provide synthesis of assembly plans and product decompositions into assembly units. Such systems as ASPE, RAPID, XAP / 1, FLAPS, Archimedes, PRELEIDES, HAP, etc. can be given, as an example. These experimental developments did not get widespread use in industry, since they are based on the models of products with limited adequacy and require an expert’s active involvement in preparing initial information. The design tools for the state-of-the-art full-featured CAD/CAM systems (Siemens NX, Dassault CATIA and PTC Creo Elements / Pro), which are designed to provide CAAP, mainly take into account the geometric constraints that the design imposes on design solutions. These systems often synthesize technologically incorrect assembly sequences in which known technological heuristics are violated, for example orderliness in accuracy, consistency with the system of dimension chains, etc.An AssemBL software application package has been developed for a structured analysis of products and a synthesis of assembly plans and decompositions. The AssemBL uses a hyper-graph model of a product that correctly describes coherent and sequential assembly operations and processes. In terms of the hyper-graph model, an assembly operation is described as shrinkage of edge, an assembly plan is a sequence of shrinkages that converts a hyper-graph into the point, and a decomposition of product into assembly units is a hyper-graph partition into sub-graphs.The AssemBL solves the problem of minimizing the number of direct checks for geometric solvability when assembling complex products. This task is posed as a plus-sum two-person game of bicoloured brushing of an ordered set. In the paradigm of this model, the brushing operation is to check a certain structured fragment for solvability by collision detection methods. A rational brushing strategy minimizes the number of such checks.The package is integrated into the Siemens NX 10.0 computer-aided design system. This solution allowed us to combine specialized AssemBL tools with a developed toolkit of one of the most powerful and popular integrated CAD/CAM /CAE systems.

scholarly journals Thiophene-Based Trimers and Their Bioapplications: An Overview

Polymers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1977
Author(s):  
Lorenzo Vallan ◽  
Emin Istif ◽  
I. Jénnifer Gómez ◽  
Nuria Alegret ◽  
Daniele Mantione
Keyword(s):  
Optical Properties ◽  
Medical Research ◽  
Turning Point ◽  
State Of The Art ◽  
Thiophene Ring ◽  
Functional Polymers ◽  
Direct Application ◽  
Synthetic Methods ◽  
Scientific Interest ◽  
Chemical Modifications

Certainly, the success of polythiophenes is due in the first place to their outstanding electronic properties and superior processability. Nevertheless, there are additional reasons that contribute to arouse the scientific interest around these materials. Among these, the large variety of chemical modifications that is possible to perform on the thiophene ring is a precious aspect. In particular, a turning point was marked by the diffusion of synthetic strategies for the preparation of terthiophenes: the vast richness of approaches today available for the easy customization of these structures allows the finetuning of their chemical, physical, and optical properties. Therefore, terthiophene derivatives have become an extremely versatile class of compounds both for direct application or for the preparation of electronic functional polymers. Moreover, their biocompatibility and ease of functionalization make them appealing for biology and medical research, as it testifies to the blossoming of studies in these fields in which they are involved. It is thus with the willingness to guide the reader through all the possibilities offered by these structures that this review elucidates the synthetic methods and describes the full chemical variety of terthiophenes and their derivatives. In the final part, an in-depth presentation of their numerous bioapplications intends to provide a complete picture of the state of the art.

Privacy-preserving Streaming Truth Discovery in Crowdsourcing with Differential Privacy

2021 ◽  
pp. 1-1
Author(s):  
Dan Wang ◽  
Ju Ren ◽  
Zhibo Wang ◽  
Xiaoyi Pang ◽  
Yaoxue Zhang ◽  
...  
Keyword(s):  
Differential Privacy ◽  
Privacy Preserving ◽  
Truth Discovery

Privacy-preserving collaborative filtering algorithm based on local differential privacy

2021 ◽  
Vol 18 (11) ◽  
pp. 42-60
Author(s):  
Ting Bao ◽  
Lei Xu ◽  
Liehuang Zhu ◽  
Lihong Wang ◽  
Ruiguang Li ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

Privacy-Preserving Collaborative Web Services QoS Prediction via Differential Privacy

2017 ◽  
pp. 200-214 ◽  
Author(s):  
Shushu Liu ◽  
An Liu ◽  
Zhixu Li ◽  
Guanfeng Liu ◽  
Jiajie Xu ◽  
...  
Keyword(s):  
Web Services ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Qos Prediction

EPPD: Efficient and privacy-preserving proximity testing with differential privacy techniques

2016 ◽  
Author(s):  
Cheng Huang ◽  
Rongxing Lu ◽  
Hui Zhu ◽  
Jun Shao ◽  
Abdulrahman Alamer ◽  
...  
Keyword(s):  
Differential Privacy ◽  
Privacy Preserving
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