1,763 research outputs found
Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation
The growing expanse of e-commerce and the widespread availability of online
databases raise many fears regarding loss of privacy and many statistical
challenges. Even with encryption and other nominal forms of protection for
individual databases, we still need to protect against the violation of privacy
through linkages across multiple databases. These issues parallel those that
have arisen and received some attention in the context of homeland security.
Following the events of September 11, 2001, there has been heightened attention
in the United States and elsewhere to the use of multiple government and
private databases for the identification of possible perpetrators of future
attacks, as well as an unprecedented expansion of federal government data
mining activities, many involving databases containing personal information. We
present an overview of some proposals that have surfaced for the search of
multiple databases which supposedly do not compromise possible pledges of
confidentiality to the individuals whose data are included. We also explore
their link to the related literature on privacy-preserving data mining. In
particular, we focus on the matching problem across databases and the concept
of ``selective revelation'' and their confidentiality implications.Comment: Published at http://dx.doi.org/10.1214/088342306000000240 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Towards Practical Privacy Preserving Technology Adoption Analysis Service Platform
Technology adoption analysis is one of the key exercises in managing technology innovation and diffusion. In this paper, we present a service platform for technology adoption analysis, with aim tailored to provide service provisioning to potential technology users and providers. With two service models provided in this platform, a practical privacy preserving framework is developed to help relieve privacy concerns of the platform participants. To illustrate the feasibility of the privacy preserving framework of this platform, an adoption process for RFID technology adoption analysis in logistics and supply chain management is presented to identify key sensitive attributes for background knowledge leading to unique identification of an individual or company.published_or_final_versio
Confidential Machine Learning on Untrusted Platforms: a Survey
With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified framework, we highlight the critical challenges and innovations in outsourcing machine learning confidentially. We focus on the cryptographic approaches for confidential machine learning (CML), primarily on model training, while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing environment. The discussion will take a holistic way to consider a rich context of the related threat models, security assumptions, design principles, and associated trade-offs amongst data utility, cost, and confidentiality
Peer-to-Peer Secure Multi-Party Numerical Computation Facing Malicious Adversaries
We propose an efficient framework for enabling secure multi-party numerical
computations in a Peer-to-Peer network. This problem arises in a range of
applications such as collaborative filtering, distributed computation of trust
and reputation, monitoring and other tasks, where the computing nodes is
expected to preserve the privacy of their inputs while performing a joint
computation of a certain function. Although there is a rich literature in the
field of distributed systems security concerning secure multi-party
computation, in practice it is hard to deploy those methods in very large scale
Peer-to-Peer networks. In this work, we try to bridge the gap between
theoretical algorithms in the security domain, and a practical Peer-to-Peer
deployment.
We consider two security models. The first is the semi-honest model where
peers correctly follow the protocol, but try to reveal private information. We
provide three possible schemes for secure multi-party numerical computation for
this model and identify a single light-weight scheme which outperforms the
others. Using extensive simulation results over real Internet topologies, we
demonstrate that our scheme is scalable to very large networks, with up to
millions of nodes. The second model we consider is the malicious peers model,
where peers can behave arbitrarily, deliberately trying to affect the results
of the computation as well as compromising the privacy of other peers. For this
model we provide a fourth scheme to defend the execution of the computation
against the malicious peers. The proposed scheme has a higher complexity
relative to the semi-honest model. Overall, we provide the Peer-to-Peer network
designer a set of tools to choose from, based on the desired level of security.Comment: Submitted to Peer-to-Peer Networking and Applications Journal (PPNA)
200
Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners
The k-nearest neighbors (k-NN) algorithm is a popular and effective
classification algorithm. Due to its large storage and computational
requirements, it is suitable for cloud outsourcing. However, k-NN is often run
on sensitive data such as medical records, user images, or personal
information. It is important to protect the privacy of data in an outsourced
k-NN system.
Prior works have all assumed the data owners (who submit data to the
outsourced k-NN system) are a single trusted party. However, we observe that in
many practical scenarios, there may be multiple mutually distrusting data
owners. In this work, we present the first framing and exploration of privacy
preservation in an outsourced k-NN system with multiple data owners. We
consider the various threat models introduced by this modification. We discover
that under a particularly practical threat model that covers numerous
scenarios, there exists a set of adaptive attacks that breach the data privacy
of any exact k-NN system. The vulnerability is a result of the mathematical
properties of k-NN and its output. Thus, we propose a privacy-preserving
alternative system supporting kernel density estimation using a Gaussian
kernel, a classification algorithm from the same family as k-NN. In many
applications, this similar algorithm serves as a good substitute for k-NN. We
additionally investigate solutions for other threat models, often through
extensions on prior single data owner systems
Prochlo: Strong Privacy for Analytics in the Crowd
The large-scale monitoring of computer users' software activities has become
commonplace, e.g., for application telemetry, error reporting, or demographic
profiling. This paper describes a principled systems architecture---Encode,
Shuffle, Analyze (ESA)---for performing such monitoring with high utility while
also protecting user privacy. The ESA design, and its Prochlo implementation,
are informed by our practical experiences with an existing, large deployment of
privacy-preserving software monitoring.
(cont.; see the paper
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