3,667 research outputs found
Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation
With the wide deployment of public cloud computing infrastructures, using
clouds to host data query services has become an appealing solution for the
advantages on scalability and cost-saving. However, some data might be
sensitive that the data owner does not want to move to the cloud unless the
data confidentiality and query privacy are guaranteed. On the other hand, a
secured query service should still provide efficient query processing and
significantly reduce the in-house workload to fully realize the benefits of
cloud computing. We propose the RASP data perturbation method to provide secure
and efficient range query and kNN query services for protected data in the
cloud. The RASP data perturbation method combines order preserving encryption,
dimensionality expansion, random noise injection, and random projection, to
provide strong resilience to attacks on the perturbed data and queries. It also
preserves multidimensional ranges, which allows existing indexing techniques to
be applied to speedup range query processing. The kNN-R algorithm is designed
to work with the RASP range query algorithm to process the kNN queries. We have
carefully analyzed the attacks on data and queries under a precisely defined
threat model and realistic security assumptions. Extensive experiments have
been conducted to show the advantages of this approach on efficiency and
security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201
PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn
Preserving privacy of users is a key requirement of web-scale analytics and
reporting applications, and has witnessed a renewed focus in light of recent
data breaches and new regulations such as GDPR. We focus on the problem of
computing robust, reliable analytics in a privacy-preserving manner, while
satisfying product requirements. We present PriPeARL, a framework for
privacy-preserving analytics and reporting, inspired by differential privacy.
We describe the overall design and architecture, and the key modeling
components, focusing on the unique challenges associated with privacy,
coverage, utility, and consistency. We perform an experimental study in the
context of ads analytics and reporting at LinkedIn, thereby demonstrating the
tradeoffs between privacy and utility needs, and the applicability of
privacy-preserving mechanisms to real-world data. We also highlight the lessons
learned from the production deployment of our system at LinkedIn.Comment: Conference information: ACM International Conference on Information
and Knowledge Management (CIKM 2018
A Talk on Quantum Cryptography, or How Alice Outwits Eve
Alice and Bob wish to communicate without the archvillainess Eve
eavesdropping on their conversation. Alice, decides to take two college
courses, one in cryptography, the other in quantum mechanics. During the
courses, she discovers she can use what she has just learned to devise a
cryptographic communication system that automatically detects whether or not
Eve is up to her villainous eavesdropping. Some of the topics discussed are
Heisenberg's Uncertainty Principle, the Vernam cipher, the BB84 and B92
cryptographic protocols. The talk ends with a discussion of some of Eve's
possible eavesdropping strategies, opaque eavesdropping, translucent
eavesdropping, and translucent eavesdropping with entanglement.Comment: 31 pages, 8 figures. Revised version of a paper published in "Coding
Theory, and Cryptography: From Geheimscheimschreiber and Enigma to Quantum
Theory," (edited by David Joyner), Springer-Verlag, 1999 (pp. 144-174). To be
published with the permission of Springer-Verlag in an AMS PSAPM Short Course
volume entitled "Quantum Computation.
Conditionals in Homomorphic Encryption and Machine Learning Applications
Homomorphic encryption aims at allowing computations on encrypted data
without decryption other than that of the final result. This could provide an
elegant solution to the issue of privacy preservation in data-based
applications, such as those using machine learning, but several open issues
hamper this plan. In this work we assess the possibility for homomorphic
encryption to fully implement its program without relying on other techniques,
such as multiparty computation (SMPC), which may be impossible in many use
cases (for instance due to the high level of communication required). We
proceed in two steps: i) on the basis of the structured program theorem
(Bohm-Jacopini theorem) we identify the relevant minimal set of operations
homomorphic encryption must be able to perform to implement any algorithm; and
ii) we analyse the possibility to solve -- and propose an implementation for --
the most fundamentally relevant issue as it emerges from our analysis, that is,
the implementation of conditionals (requiring comparison and selection/jump
operations). We show how this issue clashes with the fundamental requirements
of homomorphic encryption and could represent a drawback for its use as a
complete solution for privacy preservation in data-based applications, in
particular machine learning ones. Our approach for comparisons is novel and
entirely embedded in homomorphic encryption, while previous studies relied on
other techniques, such as SMPC, demanding high level of communication among
parties, and decryption of intermediate results from data-owners. Our protocol
is also provably safe (sharing the same safety as the homomorphic encryption
schemes), differently from other techniques such as
Order-Preserving/Revealing-Encryption (OPE/ORE).Comment: 14 pages, 1 figure, corrected typos, added introductory pedagogical
section on polynomial approximatio
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