65,508 research outputs found
Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping
Big data collection practices using Internet of Things (IoT) pervasive
technologies are often privacy-intrusive and result in surveillance, profiling,
and discriminatory actions over citizens that in turn undermine the
participation of citizens to the development of sustainable smart cities.
Nevertheless, real-time data analytics and aggregate information from IoT
devices open up tremendous opportunities for managing smart city
infrastructures. The privacy-enhancing aggregation of distributed sensor data,
such as residential energy consumption or traffic information, is the research
focus of this paper. Citizens have the option to choose their privacy level by
reducing the quality of the shared data at a cost of a lower accuracy in data
analytics services. A baseline scenario is considered in which IoT sensor data
are shared directly with an untrustworthy central aggregator. A grouping
mechanism is introduced that improves privacy by sharing data aggregated first
at a group level compared as opposed to sharing data directly to the central
aggregator. Group-level aggregation obfuscates sensor data of individuals, in a
similar fashion as differential privacy and homomorphic encryption schemes,
thus inference of privacy-sensitive information from single sensors becomes
computationally harder compared to the baseline scenario. The proposed system
is evaluated using real-world data from two smart city pilot projects. Privacy
under grouping increases, while preserving the accuracy of the baseline
scenario. Intra-group influences of privacy by one group member on the other
ones are measured and fairness on privacy is found to be maximized between
group members with similar privacy choices. Several grouping strategies are
compared. Grouping by proximity of privacy choices provides the highest privacy
gains. The implications of the strategy on the design of incentives mechanisms
are discussed
Enforcing Secure and Privacy-Preserving Information Brokering in Distributed Information Sharing
Today’s organizations raise an increasing need for information sharing via on-demand access. Information Brokering Systems (IBSs) have been proposed to connect large-scale loosely-federated data sources via a brokering overlay, in which the brokers make routing decisions to direct client queries to the requested data servers. Many existing IBSs assume that brokers are trusted and thus only adopt server-side access control for data confidentiality. However, privacy of data location and data consumer can still be inferred from metadata (such as query and access control rules) exchanged within the IBS, but little attention has been put on its protection. In this article, we propose a novel approach to preserve privacy of multiple stakeholders involved in the information brokering process. We are among the first to formally define two privacy attacks, namely attribute-correlation attack and inference attack, and propose two countermeasure schemes automaton segmentation and query segment encryption to securely share the routing decision making responsibility among a selected set brokering servers. With comprehensive security analysis and experimental results, we show that our approach seamlessly integrates security enforcement with query routing to provide system-wide security with insignificant overhead
Privacy preserving algorithms for newly emergent computing environments
Privacy preserving data usage ensures appropriate usage of data without compromising sensitive information. Data privacy is a primary requirement since customers' data is an asset to any organization and it contains customers' private information. Data seclusion cannot be a solution to keep data private. Data sharing as well as keeping data private is important for different purposes, e.g., company welfare, research, business etc. A broad range of industries where data privacy is mandatory includes healthcare, aviation industry, education system, federal law enforcement, etc.In this thesis dissertation we focus on data privacy schemes in emerging fields of computer science, namely, health informatics, data mining, distributed cloud, biometrics, and mobile payments. Linking and mining medical records across different medical service providers are important to the enhancement of health care quality. Under HIPAA regulation keeping medical records private is important. In real-world health care databases, records may well contain errors. Linking the error-prone data and preserving data privacy at the same time is very difficult. We introduce a privacy preserving Error-Tolerant Linking Algorithm to enable medical records linkage for error-prone medical records. Mining frequent sequential patterns such as, patient path, treatment pattern, etc., across multiple medical sites helps to improve health care quality and research. We propose a privacy preserving sequential pattern mining scheme across multiple medical sites. In a distributed cloud environment resources are provided by users who are geographically distributed over a large area. Since resources are provided by regular users, data privacy and security are main concerns. We propose a privacy preserving data storage mechanism among different users in a distributed cloud. Managing secret key for encryption is difficult in a distributed cloud. To protect secret key in a distributed cloud we propose a multilevel threshold secret sharing mechanism. Biometric authentication ensures user identity by means of user's biometric traits. Any individual's biometrics should be protected since biometrics are unique and can be stolen or misused by an adversary. We present a secure and privacy preserving biometric authentication scheme using watermarking technique. Mobile payments have become popular with the extensive use of mobile devices. Mobile applications for payments needs to be very secure to perform transactions and at the same time needs to be efficient. We design and develop a mobile application for secure mobile payments. To secure mobile payments we focus on user's biometric authentication as well as secure bank transaction. We propose a novel privacy preserving biometric authentication algorithm for secure mobile payments
MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning
Federated Learning (FL), a distributed machine learning paradigm, has been
adapted to mitigate privacy concerns for customers. Despite their appeal, there
are various inference attacks that can exploit shared-plaintext model updates
to embed traces of customer private information, leading to serious privacy
concerns. To alleviate this privacy issue, cryptographic techniques such as
Secure Multi-Party Computation and Homomorphic Encryption have been used for
privacy-preserving FL. However, such security issues in privacy-preserving FL
are poorly elucidated and underexplored. This work is the first attempt to
elucidate the triviality of performing model corruption attacks on
privacy-preserving FL based on lightweight secret sharing. We consider
scenarios in which model updates are quantized to reduce communication overhead
in this case, where an adversary can simply provide local parameters outside
the legal range to corrupt the model. We then propose the MUD-PQFed protocol,
which can precisely detect malicious clients performing attacks and enforce
fair penalties. By removing the contributions of detected malicious clients,
the global model utility is preserved to be comparable to the baseline global
model without the attack. Extensive experiments validate effectiveness in
maintaining baseline accuracy and detecting malicious clients in a fine-grained
mannerComment: 13 pages,13 figure
Efficient Privacy Preserving Distributed Clustering Based on Secret Sharing
In this paper, we propose a privacy preserving distributed
clustering protocol for horizontally partitioned data based on a very efficient
homomorphic additive secret sharing scheme. The model we use
for the protocol is novel in the sense that it utilizes two non-colluding
third parties. We provide a brief security analysis of our protocol from
information theoretic point of view, which is a stronger security model.
We show communication and computation complexity analysis of our
protocol along with another protocol previously proposed for the same
problem. We also include experimental results for computation and communication
overhead of these two protocols. Our protocol not only outperforms
the others in execution time and communication overhead on
data holders, but also uses a more efficient model for many data mining
applications
Federated Learning with Reduced Information Leakage and Computation
Federated learning (FL) is a distributed learning paradigm that allows
multiple decentralized clients to collaboratively learn a common model without
sharing local data. Although local data is not exposed directly, privacy
concerns nonetheless exist as clients' sensitive information can be inferred
from intermediate computations. Moreover, such information leakage accumulates
substantially over time as the same data is repeatedly used during the
iterative learning process. As a result, it can be particularly difficult to
balance the privacy-accuracy trade-off when designing privacy-preserving FL
algorithms. In this paper, we introduce Upcycled-FL, a novel federated learning
framework with first-order approximation applied at every even iteration. Under
this framework, half of the FL updates incur no information leakage and require
much less computation. We first conduct the theoretical analysis on the
convergence (rate) of Upcycled-FL, and then apply perturbation mechanisms to
preserve privacy. Experiments on real-world data show that Upcycled-FL
consistently outperforms existing methods over heterogeneous data, and
significantly improves privacy-accuracy trade-off while reducing 48% of the
training time on average
Secrecy-Preserving Reasoning Over Entailment Systems: Theory and Applications
Privacy, copyright, security and other concerns make it essential for many distributed web applications to support selective sharing of information while, at the same time, protecting sensitive knowledge. Secrecypreserving reasoning refers to the answering of queries against a knowledge base involving inference that uses sensitive knowledge without revealing it. We present a general framework for secrecy-preserving reasoning over arbitrary entailment systems. This framework enables reasoning with hierarchical ontologies, propositional logic knowledge bases (over arbitrary logics) and RDFS knowledge bases containing sensitive information that needs to be protected. We provide an algorithm that, given a knowledge base over an effectively enumerable entailment system, and a secrecy set over it, defines a maximally informative secrecypreserving reasoner. Secrecy-preserving mappings between knowledge bases that allow reusing reasoners across knowledge bases are introduced
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