Enabling Multi-level Trust in Privacy Preserving Data Mining

Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate models about aggregated data without access to precise information in individual data record. A widely studied \emph{perturbation-based PPDM} approach introduces random perturbation to individual values to preserve privacy before data is published. Previous solutions of this approach are limited in their tacit assumption of single-level trust on data miners. In this work, we relax this assumption and expand the scope of perturbation-based PPDM to Multi-Level Trust (MLT-PPDM). In our setting, the more trusted a data miner is, the less perturbed copy of the data it can access. Under this setting, a malicious data miner may have access to differently perturbed copies of the same data through various means, and may combine these diverse copies to jointly infer additional information about the original data that the data owner does not intend to release. Preventing such \emph{diversity attacks} is the key challenge of providing MLT-PPDM services. We address this challenge by properly correlating perturbation across copies at different trust levels. We prove that our solution is robust against diversity attacks with respect to our privacy goal. That is, for data miners who have access to an arbitrary collection of the perturbed copies, our solution prevent them from jointly reconstructing the original data more accurately than the best effort using any individual copy in the collection. Our solution allows a data owner to generate perturbed copies of its data for arbitrary trust levels on-demand. This feature offers data owners maximum flexibility.Comment: 20 pages, 5 figures. Accepted for publication in IEEE Transactions on Knowledge and Data Engineerin

Adaptive Representations for Tracking Breaking News on Twitter

Twitter is often the most up-to-date source for finding and tracking breaking news stories. Therefore, there is considerable interest in developing filters for tweet streams in order to track and summarize stories. This is a non-trivial text analytics task as tweets are short, and standard retrieval methods often fail as stories evolve over time. In this paper we examine the effectiveness of adaptive mechanisms for tracking and summarizing breaking news stories. We evaluate the effectiveness of these mechanisms on a number of recent news events for which manually curated timelines are available. Assessments based on ROUGE metrics indicate that an adaptive approaches are best suited for tracking evolving stories on Twitter.Comment: 8 Pag

Principles of Physical Layer Security in Multiuser Wireless Networks: A Survey

This paper provides a comprehensive review of the domain of physical layer security in multiuser wireless networks. The essential premise of physical-layer security is to enable the exchange of confidential messages over a wireless medium in the presence of unauthorized eavesdroppers without relying on higher-layer encryption. This can be achieved primarily in two ways: without the need for a secret key by intelligently designing transmit coding strategies, or by exploiting the wireless communication medium to develop secret keys over public channels. The survey begins with an overview of the foundations dating back to the pioneering work of Shannon and Wyner on information-theoretic security. We then describe the evolution of secure transmission strategies from point-to-point channels to multiple-antenna systems, followed by generalizations to multiuser broadcast, multiple-access, interference, and relay networks. Secret-key generation and establishment protocols based on physical layer mechanisms are subsequently covered. Approaches for secrecy based on channel coding design are then examined, along with a description of inter-disciplinary approaches based on game theory and stochastic geometry. The associated problem of physical-layer message authentication is also introduced briefly. The survey concludes with observations on potential research directions in this area.Comment: 23 pages, 10 figures, 303 refs. arXiv admin note: text overlap with arXiv:1303.1609 by other authors. IEEE Communications Surveys and Tutorials, 201

Function Private Predicate Encryption for Low Min-Entropy Predicates

In this work, we propose new predicate encryption schemes for zero inner-product encryption (ZIPE) and non-zero inner-product encryption (NIPE) predicates from prime-order bilinear pairings, which are both attribute and function private in the public-key setting. Our ZIPE scheme is adaptively attribute private under the standard Matrix DDH assumption for unbounded collusions. It is additionally computationally function private under a min-entropy variant of the Matrix DDH assumption for predicates sampled from distributions with superlogarithmic min-entropy. Existing (statistically) function private ZIPE schemes due to Boneh et al. [Crypto’13, Asiacrypt’13] necessarily require predicate distributions with significantly larger min-entropy in the public-key setting. Our NIPE scheme is adaptively attribute private under the standard Matrix DDH assumption, albeit for bounded collusions. It is also computationally function private under a min-entropy variant of the Matrix DDH assumption for predicates sampled from distributions with super-logarithmic min-entropy. To the best of our knowledge, existing NIPE schemes from bilinear pairings were neither attribute private nor function private. Our constructions are inspired by the linear FE constructions of Agrawal et al. [Crypto’16] and the simulation secure ZIPE of Wee [TCC’17]. In our ZIPE scheme, we show a novel way of embedding two different hard problem instances in a single secret key - one for unbounded collusion-resistance and the other for function privacy. With respect to NIPE, we introduce new techniques for simultaneously achieving attribute and function privacy. We also show natural generalizations of our ZIPE and NIPE constructions to a wider class of subspace membership, subspace non-membership and hidden-vector encryption predicates

Property Preserving Symmetric Encryption Revisited

At EUROCRYPT~2012 Pandey and Rouselakis introduced the notion of property preserving symmetric encryption which enables checking for a property on plaintexts by running a public test on the corresponding ciphertexts. Their primary contributions are: (i) a separation between `find-then-guess\u27 and `left-or-right\u27 security notions; (ii) a concrete construction for left-or-right secure orthogonality testing in composite order bilinear groups. This work undertakes a comprehensive (crypt)analysis of property preserving symmetric encryption on both these fronts. We observe that the quadratic residue based property used in their separation result is a special case of testing equality of one-bit messages, suggest a very simple and efficient deterministic encryption scheme for testing equality and show that the two security notions, find-then-guess and left-or-right, are tightly equivalent in this setting. On the other hand, the separation result easily generalizes for the equality property. So contextualized, we posit that the question of separation between security notions is property specific and subtler than what the authors envisaged; mandating further critical investigation. Next, we show that given a find-then-guess secure orthogonality preserving encryption of vectors of length 2n, there exists left-or-right secure orthogonality preserving encryption of vectors of length n, giving further evidence that find-then-guess is indeed a meaningful notion of security for property preserving encryption. Finally, we cryptanalyze the scheme for testing orthogonality. A simple distinguishing attack establishes that it is not even the weakest selective find-then-guess secure. Our main attack extracts out the subgroup elements used to mask the message vector and indicates greater vulnerabilities in the construction beyond indistinguishability. Overall, our work underlines the importance of cryptanalysis in provable security