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Proceedings of the 2005 ACM SIGMOD international conference on Management of data, 2005 We present techniques for privacy-preserving computation of multidimensional aggregates on data partitioned across multiple clients. Data from different clients is perturbed (randomized) in order to preserve privacy before it is integrated at the server. We develop formal notions of privacy obtained from data perturbation and show that our perturbation
Ramakrishnan Srikant +2 more
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Privacy-preserving Cooperative Positioning
ION GNSS+, The International Technical Meeting of the Satellite Division of The Institute of Navigation, 2020We address the issue of user privacy in the context of “collaborative” positioning, wherein information is passed between and processed by multiple cooperative agents with the goal of achieving high levels of positioning accuracy. In particular, we evaluate the feasibility of applying a layer of encryption to a linear least squares (LS) algorithm for ...
Gerald LaMountain +2 more
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Econometrics with Privacy Preservation
Operations Research, 2019Many data are sensitive in areas such as finance, economics, and other social sciences. We propose an ER (encryption and recovery) algorithm that allows a central administration to do statistical i...
Ning Cai, Steven Kou
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Privacy-Preserving Pseudonyms for LoRaWAN
Proceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile NetworksInternational ...
Samuel Pélissier +5 more
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Privacy Preserving Tâtonnement
2014Leon Walras’ theory of general equilibrium put forth the notion of tâtonnement as a process by which equilibrium prices are determined. Recently, Cole and Fleischer provided tâtonnement algorithms for both the classic One-Time and Ongoing Markets with guaranteed bounds for convergence to equilibrium prices. However, in order to reach equilibrium, trade
John Ross Wallrabenstein, Chris Clifton
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Communications of the ACM, 2020
Can you answer a poll without revealing your true preferences and have the results of the poll still be accurate?
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Can you answer a poll without revealing your true preferences and have the results of the poll still be accurate?
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2018
In this chapter, we illustrate main results on privacy-preserving embeddings. Here, security properties of embeddings are analyzed by considering two possible scenarios for their use. In the first case, a client submits a query containing sensitive information to a server, which should respond to the query without gaining access to the private ...
Tiziano Bianchi +3 more
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In this chapter, we illustrate main results on privacy-preserving embeddings. Here, security properties of embeddings are analyzed by considering two possible scenarios for their use. In the first case, a client submits a query containing sensitive information to a server, which should respond to the query without gaining access to the private ...
Tiziano Bianchi +3 more
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Workflow Abstraction for Privacy Preservation [PDF]
, 2007 This work is in line with the CoopFlow approach dedicated for workflow advertisement, interconnection, and cooperation in the context of virtual enterprises. To support cooperation, one has to deal with the partners' privacy respect. In fact, cooperation needs a certain degree of inter-visibility in order to perform interactions and data exchange ...
Chebbi, Issam, Tata, Samir
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2009
Data mining has evolved from a need to make sense of the enormous amounts of data generated by organizations. But data mining comes with its own cost, including possible threats to the confidentiality and privacy of individuals. This chapter presents a background on privacy-preserving data mining (PPDM) and the related field of statistical disclosure ...
Mohammad Saad Al-Ahmadi +1 more
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Data mining has evolved from a need to make sense of the enormous amounts of data generated by organizations. But data mining comes with its own cost, including possible threats to the confidentiality and privacy of individuals. This chapter presents a background on privacy-preserving data mining (PPDM) and the related field of statistical disclosure ...
Mohammad Saad Al-Ahmadi +1 more
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Practical Secure Aggregation for Privacy-Preserving Machine Learning
IACR Cryptology ePrint Archive, 2017Keith Bonawitz +8 more
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