IEEE Organizations related to Differential Privacy

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Conferences related to Differential Privacy

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2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS)

algorithms and data structures, computational complexity, cryptography, computational learningtheory, economics and computation, parallel and distributed algorithms, quantum computing,computational geometry, computational applications of logic, algorithmic graph theory andcombinatorics, optimization, randomness in computing, approximation algorithms, algorithmiccoding theory, algebraic computation, and theoretical aspects of areas such as networks,privacy, information retrieval, computational biology, and databases.

  • 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS)

    algorithms and data structures, computational complexity, cryptography, computational learning theory, economics and computation, parallel and distributed algorithms, quantum computing, computational geometry, computational applications of logic, algorithmic graph theory and combinatorics, optimization, randomness in computing, approximation algorithms, algorithmic coding theory, algebraic computation, and theoretical aspects of areas such as networks, privacy, information retrieval, computational biology, and databases.

  • 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS)

    Papers presenting new and original research on theory of computation are sought. Typical butnot exclusive topics of interest include: algorithms and data structures, computationalcomplexity, cryptography, computational learning theory, computational game theory, paralleland distributed algorithms, quantum computing, computational geometry, computationalapplications of logic, algorithmic graph theory and combinatorics, optimization, randomness incomputing, approximation algorithms, algorithmic coding theory, algebraic computation, andtheoretical aspects of areas such as networks, privacy, information retrieval, computationalbiology, and databases. Papers that broaden the reach of the theory of computing, or raiseimportant problems that can benefit from theoretical investigation and analysis, are encouraged.

  • 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS)

    Papers presenting new and original research on theory of computation are sought. Typical but not exclusive topics of interest include: algorithms and data structures, computational complexity, cryptography, computational learning theory, computational game theory, parallel and distributed algorithms, quantum computing, computational geometry, computational applications of logic, algorithmic graph theory and combinatorics, optimization, randomness in computing, approximation algorithms, algorithmic coding theory, algebraic computation, and theoretical aspects of areas such as networks, privacy, information retrieval, computational biology, and databases. Papers that broaden the reach of the theory of computing, or raise important problems that can benefit from theoretical investigation and analysis, are encouraged.

  • 2015 IEEE 56th Annual Symposium on Foundations of Computer Science (FOCS)

    Papers presenting new and original research on theory of computation are sought. Typical but not exclusive topics of interest include: algorithms and data structures, computational complexity, cryptography, computational learning theory, computational game theory, parallel and distributed algorithms, quantum computing, computational geometry, computational applications of logic, algorithmic graph theory and combinatorics, optimization, randomness in computing, approximation algorithms, algorithmic coding theory, algebraic computation, and theoretical aspects of areas such as networks, privacy, information retrieval, computational biology, and databases. Papers that broaden the reach of the theory of computing, or raise important problems that can benefit from theoretical investigation and analysis, are encouraged.

  • 2014 IEEE 55th Annual Symposium on Foundations of Computer Science (FOCS)

    Mathematical research on the foundations of computer science, including algorithms, complexity, and their applications to other fields of computer science and other sciences

  • 2013 IEEE 54th Annual Symposium on Foundations of Computer Science (FOCS)

    Mathematical research on the foundations of computer science, including algorithms, complexity, and their applications to other fields of computer science and other sciences

  • 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science (FOCS)

    The 53rd Annual Symposium on Foundations of Computer Science (FOCS 2012), sponsored by the IEEE Computer Society Technical Committee on Mathematical Foundations of Computing, will be held at the Hotel Zozo in Palm Springs, CA, October 20-23, 2012. A series of tutorial presentations will be given. Papers presenting new and original research on the theory of computation are sought, including papers that broaden the reach of computer science theory, or raise important problems.

  • 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science (FOCS)

    The 512nd Annual Symposium on Foundations of Computer Science (FOCS2011), sponsored by the IEEE Computer Society Technical Committee on Mathematical Foundations of Computing, will be held at the Hotel Zozo in Palm Springs, CA, October 23-25, 2011. A series of tutorial presentations will be given on October 22. Papers presenting new and original research on the theory of computation are sought, including papers that broaden the reach of computer science theory, or raise important problems.

  • 2010 IEEE 51st Annual Symposium on Foundations of Computer Science (FOCS)

    The 51st Annual Symposium on Foundations of Computer Science (FOCS2010), sponsored by the IEEE Computer Society Technical Committee on Mathematical Foundations of Computing, will be held at the Monte Carlo Hotel in Las Vegas, Nevada, October 24-26, 2010. A series of tutorial presentations will be given on October 23. Papers presenting new and original research on the theory of computation are sought, including papers that broaden the reach of computer science theory, or raise important problems that can ben

  • 2009 IEEE 50th Annual Symposium on Foundations of Computer Science - FOCS

    The 50th Annual Symposium on Foundations of Computer Science (FOCS2009), sponsored by the IEEE Computer Society Technical Committee on Mathematical Foundations of Computing, will be held in at the Renaissance Atlanta Hotel Downtown in Atlanta, GA, October 24-27, 2009. Papers presenting new and original research on theory of computation are sought. Typical but not exclusive topics of interest include: algorithms and data structures, computational complexity, cryptography, computational geometry, computationa

  • 2008 IEEE 49th Annual Symposium on Foundations of Computer Science - FOCS

  • 2007 IEEE 48th Annual Conference on Foundations of Computer Science - FOCS

  • 2006 IEEE 47th Annual Conference on Foundations of Computer Science - FOCS

  • 2005 IEEE 46th Annual Conference on Foundations of Computer Science - FOCS


2019 IEEE Information Theory Workshop (ITW)

The scope of IEEE ITW2019 will be in all areas of Information Theory. Fields of interest include, but are not limited to Information Theory for Cyber-Physical Systems, Modern Coding Theory, and Security, Privacy and Trust.

  • 2018 IEEE Information Theory Workshop (ITW)

    Original papers on Information and Coding Theory are encouraged for submission. The scope of submission includes, but is not limited toInformation Theory and its ApplicationsFrontiers of Coding Theory and PracticeBoundaries between Information Theory and Data Science, Biology and Signal ProcessingNetwork Information Theory Network Coding and Distributed StorageInformation Theoretic Security

  • 2017 IEEE Information Theory Workshop (ITW)

    The scope of IEEE ITW2017 will be in all areas of information theory with special emphasis on the following:• Information Theory for Content Distribution - Distributed data storage - Peer-to-peer network coded broadcasting - Coded caching for wireless and wireline transmissions - Delay-constrained communications• Information Theory and Biology - Information theory and intercellular communication - Information theory and neuroscience - Information-theoretical analysis of biologically-inspired communication systems• Information Theory and Quantum Communication - Quantum information - Quantum computation - Quantum cryptography• Information Theory and Coding for Memories - Inter-cell interference in nonvolatile memories - Rank modulation and constrained codes for nonvolatile memories

  • 2016 IEEE Information Theory Workshop (ITW)

    Broad scope of information theory.

  • 2015 IEEE Information Theory Workshop (ITW)

    The Information Theory Workshop 2015 in Jerusalem will cover all fields of information, with special emphasis on the interaction between information theory and computer science, signal procession and networks.

  • 2014 IEEE Information Theory Workshop (ITW)

    ITW2014 is a forum for technical exchange among scientists and engineers working on the fundamentals of information theory. The agenda is broad and will cover the diverse topics that information theory presently impacts. There will be both invited and contributed sessions.

  • 2013 IEEE Information Theory Workshop (ITW 2013)

    The scope of the workshop includes, but is not limited to the following topics: BioInformatics, Communication, Machine Learning, Security, Spectrum Sharing, Coding, Compression, Networks, Signal Processing, Statistics.

  • 2012 IEEE Information Theory Workshop (ITW 2012)

    The past decade has seen an exponential increase in the data stored in distributed locations in various forms including corporate & personal data, multimedia, and medical data in repositories. The grand challenge is to store, process and transfer this massive amount of data, efficiently and securely over heterogeneous communication networks.

  • 2010 IEEE Information Theory Workshop (ITW 2010)

    Algebraic Methods in Communications Technology

  • 2009 IEEE Information Theory Workshop (ITW 2009)

    Covers the most relevant topics in Information Theory and Coding Theory of interest to the most recent applications to wireless networks, sensor networks, and biology

  • 2008 IEEE Information Theory Workshop (ITW 2008)

    This workshop will take a brief look into the recent information theory past to commemorate the 60th anniversary of Shannon's landmark paper, and then proceed to explore opportunities for information theory research in quantum computation, biology, statistics, and computer science.

  • 2006 IEEE Information Theory Workshop (ITW 2006)


2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

The conference solicits experimental and theoretical works on social network analysis and mining along with their application to real life situations.

  • 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

    The conference solicits experimental and theoretical works on social network analysis and mining along with their application to real life situations.

  • 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

    social networks and mining

  • 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

    The conference will consider papers in all areas of social networks and mining. In recent years, social network research has advanced significantly; the development of sophisticated techniques for Social Network Analysis and Mining (SNAM) has been highly influenced by the online social Web sites, email logs, phone logs and instant messaging systems, which are widely analyzed using graph theory and machine learning techniques. People perceive the Web increasingly as a social medium that fosters interaction among people, sharing of experiences and knowledge, group activities, community formation and evolution. This has led to a rising prominence of SNAM in academia, politics, homeland security and business. This follows the pattern of known entities of our society that have evolved into networks in which actors are increasingly dependent on their structural embedding.

  • 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

    The international conference on Advances in Social Network Analysis and Mining (ASONAM 2015) will primarily provide an interdisciplinary venue that will bring together practitioners and researchers from a variety of SNAM fields to promote collaborations and exchange of ideas and practices. ASONAM 2015 is intended to address important aspects with a specific focus on the emerging trends and industry needs associated with social networking analysis and mining. The conference solicits experimental and theoretical works on social network analysis and mining along with their application to real life situations.

  • 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

    The IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM) provides a premier interdisciplinary forum to bring together researchers and practitioners from all social networking analysis and mining related fields for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. ASONAM 2014 seeks to address important challenging problems with a specific focus on the emerging trends and industry needs associated with social networking analysis and mining. The conference solicits experimental and theoretical findings along with their real-world applications. General areas of interest to ASONAM 2014 include the design, analysis and implementation of social networking theory, systems and applications from computer science, mathematics, communications, business administration, sociology, psychology, anthropology, applied linguistics, biology and medicine.

  • 2013 International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

    People perceive the Web increasingly as a social medium that fosters interaction among people, sharing of experiences and knowledge, group activities, community formation and evolution. This has led to a rising prominence of Social Network Analysis and Mining (SNAM) in academia, politics, homeland security and business. The 2013 international conference on Advances in Social Network Analysis and Mining (ASONAM-13 will primarily provide an interdisciplinary venue brings together practitioners and researchers from a variety of SNAM fields to promote collaborations and exchange of ideas and practices. The conference will address important aspects with a specific focus on the emerging trends and industry needs associated with social networking analysis and mining. The conference solicits experimental and theoretical works on social network analysis and mining with their application to real life situations.

  • 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)

    In recent years, social network research has advanced significantly; the development of sophisticated techniques for Social Network Analysis and Mining (SNAM) has been highly influenced by the online social Web sites, email logs, phone logs and instant messaging systems, which are widely analyzed using graph theory and machine learning techniques. People perceive the Web increasingly as a social medium that fosters interaction among people, sharing of experiences and knowledge, group activities, community formation and evolution. This has led to a rising prominence of SNAM in academia, politics, homeland security and business.

  • 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011)

    The international conference on Advances in Social Network Analysis and Mining (ASONAM 2011) will primarily provide an interdisciplinary venue that will bring together practitioners and researchers from a variety of SNAM fields to promote collaborations and exchange of ideas and practices. ASONAM 2011 is intended to address important aspects with a specific focus on the emerging trends and industry needs associated with social networking analysis and mining. The conference solicits experimental and theoreti

  • 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2010)

    The international conference on Advances in Social Network Analysis and Mining (ASONAM 2010) will primarily provide an interdisciplinary venue that will bring together practitioners and researchers from a variety of SNAM fields to promote collaborations and exchange of ideas and practices. ASONAM 2010 is intended to address important aspects with a specific focus on the emerging trends and industry needs associated with social networking analysis and mining. The conference solicits experimental and theoreti

  • 2009 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2009)

    In recent years, social network research has advanced significantly; the development of sophisticated techniques for Social Network Analysis and Mining (SNAM) has been highly influenced by the online social websites, email logs, phone logs and instant messageing systems, which are widely analyzed using graph theory and machine learning techniques. People perceive teh web increasingly as a social medium that fosters interaction among people, sharing of experiences and knowledge, group activities, community


2012 International Conference on Privacy, Security, Risk and Trust (PASSAT)

The 4th International Conference on Information Privacy, Security, Risk and Trust will be held at Amsterdam, The Netherlands. The aim is to provide an international forum for information privacy, risk, trust, and security researchers and practitioners to explore solutions to profound challenges



Periodicals related to Differential Privacy

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No periodicals are currently tagged "Differential Privacy"


Most published Xplore authors for Differential Privacy

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Xplore Articles related to Differential Privacy

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Differential Privacy Protection Recommendation Algorithm Based on Student Learning Behavior

2018 IEEE 15th International Conference on e-Business Engineering (ICEBE), 2018

Traditional collaborative filtering recommendation algorithm based on learning resources use a large amount of student personal information and behavior information. This will put the user's privacy at risks since that students' information can be mined by analyzing the recommendation results. Considering that differential privacy theory can effectively protect user privacy through strict mathematical definition and maximum background knowledge assumptions, this ...


Secure Medical Data Collection via Local Differential Privacy

2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2018

As the volume of medical data mining increases, so do the need to preserve patient privacy. And the exposure of medical data may degrade the level of health care service and reduce the trust of patients. Local Differential Privacy (LDP) was proposed to solve problems in the context of local privacy, by which data collectors are hardly to get exact ...


Deep Q-Network Based Route Scheduling for TNC Vehicles with Passengers’ Location Differential Privacy

IEEE Internet of Things Journal, None

The transportation network company (TNC) services efficiently pair the passengers with the vehicles/drivers through mobile applications such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers’ revenues. From the TNC drivers’ side, the most critical question ...


Multi-Party High-Dimensional Data Publishing under Differential Privacy

IEEE Transactions on Knowledge and Data Engineering, None

In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties collectively generate a synthetic integrated dataset while satisfying $\varepsilon$-differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties ...


DRAKO: Differentially pRivate Algorithm to meet K-anonymity for Online portal service

2018 IEEE International Conference on Big Data (Big Data), 2018

Digital data on the Web are nowadays regarded significant sources of information for marketing and user profiling, etc. However, digital data are risky sources of privacy violation. To address privacy breaches, we can use differential privacy, which has become the de facto standard for privacy protection in statistical databases. However, problems need to be solved, including those related to noise ...


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Educational Resources on Differential Privacy

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IEEE.tv Videos

Honors 2020: Cynthia Dwork Wins the IEEE Richard W. Hamming Medal
How Symmetry Constrains Evolutionary Optimizers: A Black Box Differential Evolution Case Study - IEEE Congress on Evolutionary Computation 2017
Understanding Differential Evolution
MicroApps: Measurement Advances for Differential and I/Q Devices (Agilent Technologies)
Ponnuthurai Nagaratnam Suganthan - Differential Evolution
IEEE @ SXSW 2015 - A Framework for Privacy by Design
Designing Privacy Into Internet Protocols - Juan Carlos Zuniga keynote
Micro-Apps 2013: Determining Circuit Material Dielectric Constant from Phase Measurements
When Do We Resort to EC in the Communications Industry, and What is Needed in the Future? - IEEE Congress on Evolutionary Computation 2017
Luca Bolognini: Internet of Things: Privacy and Security Challenges - WF-IoT 2015
Tutorial 2: 5G Security & Privacy - NetSoft 2020 Conference
Geometry of Robot Motion - ICRA 2020
IEEE Future Directions: What is the Internet of Things?
Hardware Detection in Implantable Media Devices Using Adiabatic Computing - S. Dinesh Kumar - ICRC 2018
IEEE @ SXSW 2015 - Lessons from Africa: Relationships Over Privacy
Antonio Skarmeta: Security and Privacy in the Internet of Things - WF-IoT 2015
IEEE World Forum on Internet of Things - Milan, Italy - Sara Foresti - Data Security and Privacy in the Internet of Things - Part 1
Privacy, security, and innovation challenges in different aspects of IoT - Panel from ETAP Forum, February 2016
Glenn Fink on Priorities for IoT Security and Privacy From Here to 2020: End to End Trust and Security Workshop for the Internet of Things 2016
Security and Privacy in a World of Connected Devices

IEEE-USA E-Books

  • Differential Privacy Protection Recommendation Algorithm Based on Student Learning Behavior

    Traditional collaborative filtering recommendation algorithm based on learning resources use a large amount of student personal information and behavior information. This will put the user's privacy at risks since that students' information can be mined by analyzing the recommendation results. Considering that differential privacy theory can effectively protect user privacy through strict mathematical definition and maximum background knowledge assumptions, this paper proposes a differential privacy collaborative filtering recommendation algorithm based on learner behavior similarity. By adding noise obeying the Laplace distribution to the learner behavior similarity matrix, the recommendation accuracy rate does not reduce, as well as the privacy of student is protected effectively.

  • Secure Medical Data Collection via Local Differential Privacy

    As the volume of medical data mining increases, so do the need to preserve patient privacy. And the exposure of medical data may degrade the level of health care service and reduce the trust of patients. Local Differential Privacy (LDP) was proposed to solve problems in the context of local privacy, by which data collectors are hardly to get exact individual information. We present a secure medical data collection framework and apply our framework on synthetic data at different scale. Finally, we evaluate the performance of our work. Our experimental results show that both privacy and validity of medical data are aligned.

  • Deep Q-Network Based Route Scheduling for TNC Vehicles with Passengers’ Location Differential Privacy

    The transportation network company (TNC) services efficiently pair the passengers with the vehicles/drivers through mobile applications such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers’ revenues. From the TNC drivers’ side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings by using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network (DQN) based route scheduling algorithm for vacant TNC vehicles based on distributed framework, which makes the server closer to the terminal users and accelerates the training speed. Furthermore, we apply the geo-indistinguishability scheme based on differential privacy to preserve the sensitive location information uploaded by the passengers. We evaluate the proposed algorithm’s performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.

  • Multi-Party High-Dimensional Data Publishing under Differential Privacy

    In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties collectively generate a synthetic integrated dataset while satisfying $\varepsilon$-differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network $\mathbb{N}$ that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn $\mathbb{N}$. The core of DP-SUBN is the construction of the search frontier, which can be seen as a priori knowledge to guide the parties to update $\mathbb{N}$. Leveraging the correlations of attribute pairs, we propose exact and heuristic methods to construct the search frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies $\varepsilon$-differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.

  • DRAKO: Differentially pRivate Algorithm to meet K-anonymity for Online portal service

    Digital data on the Web are nowadays regarded significant sources of information for marketing and user profiling, etc. However, digital data are risky sources of privacy violation. To address privacy breaches, we can use differential privacy, which has become the de facto standard for privacy protection in statistical databases. However, problems need to be solved, including those related to noise parameter configuration, even before differential privacy can be applied into the real world. In this study, we introduce a linkage attack to identify a user with different nicknames for each subservice on a hue online portal service. In addition, we propose a configuration technique for the upper bound of noise parameter ε to prevent linkage attack. We demonstrate the linkage attack with experiments by using real-world online portal service data. Finally, we validate the proposed configuration technique.

  • Adaptive Differential Privacy Interactive Publishing Model Based on Dynamic Feedback

    Data publishing is very meaningful and necessary. However, there are much personal (especially sometimes sensitive) information in the datasets to be published. So, privacy preserving has become a more and more important problem what we must deal with in big data era. Because of the strong mathematics foundation, provable and quantized privacy properties, DP (differential privacy) attracts the most interests and is becoming one of the most prevalent privacy models. This paper, based on differential privacy preserving mechanism, engages in queries restriction problem in interactive privacy data publishing framework. One adaptive differential privacy interactive publishing model based on dynamic feedback model (ADP M-DF) is proposed. Then, its technological process is presented by the flow chart in detail. And, the dynamic feedback scheme is proposed with an iteration algorithm to generate new privacy budget parameter. Finally, some qualities are discussed. Analysis shows that the new model can run well with good practical meanings and provide better user query experience.

  • An Improved Differential Privacy K-Means Algorithm Based on MapReduce

    In order to solve the low clustering accuracy and the local optimum problem of the traditional differential privacy k-means algorithm, this paper proposed an improved differential privacy K-means algorithm based on MapReduce. The proposed algorithm uses Canopy to select the initial center point, and uses Laplace mechanism to realize the differential privacy protection. The simulation shows that the clustering results of the proposed algorithm outperform the traditional DP K-means in usability and convergence speed.

  • Differentially Private Publication of Vertically Partitioned Data

    In this paper, we study the problem of publishing vertically partitioned data under differential privacy, where different attributes of the same set of individuals are held by multiple parties. In this setting, with the assistance of a semi-trusted curator, the involved parties aim to collectively generate an integrated dataset while satisfying differential privacy for each local dataset. Based on the latent tree model (LTM), we present a differentially private latent tree (DPLT) approach, which is, to the best of our knowledge, the first approach to solving this challenging problem. In DPLT, the parties and the curator collaboratively identify the latent tree that best approximates the joint distribution of the integrated dataset, from which a synthetic dataset can be generated. The fundamental advantage of adopting LTM is that we can use the connections between a small number of latent attributes derived from each local dataset to capture the cross-dataset dependencies of the observed attributes in all local datasets such that the joint distribution of the integrated dataset can be learned with little injected noise and low computation and communication costs. Extensive experiments on real datasets demonstrate that DPLT offers desirable data utility with low computation and communication costs.

  • Output and Input Data Perturbations for Differentially Private Databases

    In today's ultra-connected world, the production and consumption of digital data has become immensely huge in volume. Differential privacy is a relatively new approach which attempts to provide strong privacy protection to users' data, while still maintaining outside access to data. However, the comparison of these methods in terms of performance and database suitability remains an open question. In this paper, we will provide a comprehensive comparison of input and output data perturbations for differentially private databases and evaluate the results in terms of accuracy, privacy, efficiency and scalability.

  • Privacy-preserving Distributed Data Fusion Based on Attribute Protection

    Privacy-preserving distributed data fusion is a pretreatment process in data mining involving security models. In this paper, we present a method of implementing multi-party data fusion, wherein redundant attributes of a same set of individuals are stored by multiple parties. In particular, the merged data does not suffer from background attacks or other reasoning attacks, and individual attributes are not leaked. To achieve this, we present three algorithms that satisfy K-Anonymous and differential privacy. Experimental results on real data sets suggest that the proposed algorithm can effectively preserve information in data mining tasks.



Standards related to Differential Privacy

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Jobs related to Differential Privacy

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