Undefined mesophilic mixed (DL-type) starter cultures are composed of predominantly Lactococcus lactis subspecies and 1-10% Leuconostoc spp. The composition of the Leuconostoc population in the starter culture ultimately affects the characteristics and the quality of the final product. The scientific basis for the taxonomy of dairy relevant leuconostocs can be traced back 50 years, and no documentation on the genomic diversity of leuconostocs in starter cultures exists. We present data on the Leuconostoc population in five DL-type starter cultures commonly used by the dairy industry. The analyses were performed using traditional cultivation methods, and further augmented by next-generation DNA sequencing methods. Bacterial counts for starter cultures cultivated on two different media, MRS and MPCA, revealed large differences in the relative abundance of leuconostocs. Most of the leuconostocs in two of the starter cultures were unable to grow on MRS, emphasizing the limitations of culture-based methods and the importance of careful media selection or use of culture independent methods. Pan-genomic analysis of 59 Leuconostoc genomes enabled differentiation into twelve robust lineages. The genomic analyses show that the dairy-associated leuconostocs are highly adapted to their environment, characterized by the acquisition of genotype traits, such as the ability to metabolize citrate. In particular, Leuconostoc mesenteroides subsp. cremoris display telltale signs of a degenerative evolution, likely resulting from a long period of growth in milk in association with lactococci. Great differences in the metabolic potential between Leuconostoc species and subspecies were revealed. Using targeted amplicon sequencing, the composition of the Leuconostoc population in the five commercial starter cultures was shown to be significantly different. Three of the cultures were dominated by Ln. mesenteroides subspecies cremoris. Leuconostoc pseudomesenteroides dominated in two of the cultures while Leuconostoc lactis, reported to be a major constituent in fermented dairy products, was only present in low amounts in one of the cultures. This is the first in-depth study of Leuconostoc genomics and diversity in dairy starter cultures. The results and the techniques presented may be of great value for the dairy industry.

PSD-95 PDZ domains are biologically important and promising drug targets. Here, we discover a triazole-based peptidomimetic, 10, by 'click chemistry'. Compound 10 inhibits the PDZ2/GluN2B interaction with affinity similar to tripeptide SAV and better than current small-molecules. Thus, 10 represents a new class of PSD-95 PDZ inhibitors.

The present study investigated the utility of in vitro lipolysis performance indicators drug solubilization and maximum supersaturation ratio (SRM) for their predictive use for the in vivo performance in a minipig model. The commercial Lipanthyl formulation and a series of LbDDS based on identical self-nanoemulsifying drug delivery systems (SNEDDS) containing 200 mg of fenofibrate, either dissolved or suspended, were subjected to combined gastric (pH 2) and intestinal (pH 6.5) in vitro lipolysis. Based on the solubilization profiles and SRM the rank-order SNEDDS (75% drug load) > super-SNEDDS (150% drug load, dissolved) = SNEDDS suspension (150% drug load, partially suspended) > Lipanthyl was established, with an increased likelihood of drug precipitation above SRM > 3. The in vitro performance, however, was not reproduced in vivo in a minipig model as the mean plasma concentration over time curves of all LbDDS were comparable, independent of the initial physical state of the drug. There was no correlation between the area under the solubilization-time curves (AUC in vitro ) of the intestinal step and the AUC in vivo . The study suggests careful interpretation of in vitro performance criteria and revision of LbDDS optimization towards increased solubilization.

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.

Secure multiparty protocols have been proposed to enable noncolluding parties to cooperate without a trusted server. Even though such protocols prevent information disclosure other than the objective function, they are quite costly in computation and communication. The high overhead motivates parties to estimate the utility that can be achieved as a result of the protocol beforehand. In this paper, we propose a look-ahead approach, specifically for secure multiparty protocols to achieve distributed k-anonymity, which helps parties to decide if the utility benefit from the protocol is within an acceptable range before initiating the protocol. The look-ahead operation is highly localized and its accuracy depends on the amount of information the parties are willing to share. Experimental results show the effectiveness of the proposed methods.

The fuzzy vault scheme is a well-known technique to mitigate privacy, security, and usability related problems in biometric identification applications. The basic idea is to hide biometric data along with secret information amongst randomly selected chaff points during the enrollment process. Only the owner of the biometric data who presents correct biometrics can recover the secret and identify himself. Recent research, however, has shown that the scheme is vulnerable to certain types of attacks. The recently proposed ``correlation attack'', that allows linking two vaults of the same biometric, pose serious privacy risks that have not been sufficiently addressed. The primary aim of this work is to remedy those problems by proposing a framework based on distance preserving hash functions to render the correlation attack inapplicable. We first give definitions which capture the requirements such hash functions must posses. We then propose a specific family of hash functions that fulfills these requirements and lends itself to efficient implementation. We also provide formal proofs that the proposed family of hash functions indeed protects the fuzzy vault against correlation attacks. We implement a hashed fuzzy vault using fingerprint data and investigate the effects of the proposed method on the false accept and false reject rates (FAR and FRR, respectively) extensively. Implementation results suggest that the proposed method provides a complete protection against correlation attacks at the expense of small degradation in the FRR.

Trajectories are spatio-temporal traces of moving objects which contain valuable information to be harvested by spatio-temporal data mining techniques. Applications like city traffic planning, identification of evacuation routes, trend detection, and many more can benefit from trajectory mining. However, the trajectories of individuals often contain private and sensitive information, so anyone who possess trajectory data must take special care when disclosing this data. Removing identifiers from trajectories before the release is not effective against linkage type attacks, and rich sources of background information make it even worse. An alternative is to apply transformation techniques to map the given set of trajectories into another set where the distances are preserved. This way, the actual trajectories are not released, but the distance information can still be used for data mining techniques such as clustering. In this paper, we show that an unknown private trajectory can be reconstructed using the available background information together with the mutual distances released for data mining purposes. The background knowledge is in the form of known trajectories and extra information such as the speed limit. We provide analytical results which bound the number of the known trajectories needed to reconstruct private trajectories. Experiments performed on real trajectory data sets show that the number of known samples is surprisingly smaller than the actual theoretical bounds.

We propose a solution to the secure integer comparison problem by reducing it to the secure set membership problem on a set where the elements occur in consecutive order. Our protocol provides flexibility due to this reduction such that it can easily be modified to support more complex boolean functions other than the function evaluating the 'Less Than' predicate (LT). In the paper we show one such boolean function, 'IsInInterval', which checks if an integer is in an interval (3I). We also show that our protocols can be easily extended to support shared outputs so that each party would hold a share of the result. Our generic solution can be applied to many e-commerce and data-mining applications requiring one or more such boolean functions to be computed securely. Our protocol not only is adaptable to different boolean functions but also is experimentally more efficient than the state-of-the-art integer comparison protocols supporting shared outputs.

The ability to perform scalar products of two vectors, each known to a different party, is a central problem in privacy preserving data mining and other multi-party computation problems. Ongoing search for both efficient and secure scalar product protocols has revealed that this task is not easy. In this paper we show that, indeed, scalar products can never be made secure in the information theoretical sense. We show that any attempt to make unconditionally secure scalar products will inevitably allow one of the parties to learn the other parties input vector with high probability. On the other hand, we show that under various assumptions, such as the existence of a trusted third party or the difficulty of discrete logarithms, both efficient and secure scalar products do exist. We proposed two new protocols for secure scalar products and compare their performance with existing secure scalar products.

The authors present a lightweight authentication mechanism that verifies the authenticity of code and thereby addresses the virus and malicious code problems at the hardware level eliminating the need for trusted extensions in the operating system. The technique proposed tightly integrates the authentication mechanism into the processor core. The authentication latency is hidden behind the memory access latency, thereby allowing seamless on-the-fly authentication of instructions. In addition, the proposed authentication method supports seamless encryption of code (and static data). Consequently, while providing the software users with assurance for authenticity of programs executing on their hardware, the proposed technique also protects the software manufacturers' intellectual property through encryption. The performance analysis shows that, under mild assumptions, the presented technique introduces negligible overhead for even moderate cache sizes.

One of the fundamental challenges that the data mining community faces today is privacy. The question “How are we going to do data mining without violating the privacy of individuals?” is still on the table, and research is being conducted to find efficient methods to do that. Data transformation was previously proposed as one efficient method for privacy preserving data mining when a party needs to out-source the data mining task, or when distributed data mining needs to be performed among multiple parties without each party disclosing its actual data. In this paper we study the safety of distance preserving data transformations proposed for privacy preserving data mining. We show that an adversary can recover the original data values with very high confidence via knowledge of mutual distances between data objects together with the probability distribution from which they are drawn. Experiments conducted on real and synthetic data sets demonstrate the effectiveness of the theoretical results.

Recent concerns about privacy issues motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. However, the current techniques for privacy preserving data mining suffer from high communication and computation overheads which are prohibitive considering even a modest database size. Furthermore, the proposed techniques have strict assumptions on the involved parties which need to be relaxed in order to reflect the real-world requirements. In this paper we concentrate on a distributed scenario where the data is partitioned vertically over multiple sites and the involved sites would like to perform clustering without revealing their local databases. For this setting, we propose a new protocol for privacy preserving k-means clustering based on additive secret sharing. We show that the new protocol is more secure than the state of the art. Experiments conducted on real and synthetic data sets show that, in realistic scenarios, the communication and computation cost of our protocol is considerably less than the state of the art which is crucial for data mining applications.

Privacy preserving querying and data publishing has been studied in the context of statistical databases and statistical disclosure control. Recently, large-scale data collection and integration efforts increased privacy concerns which motivated data mining researchers to investigate privacy implications of data mining and how data mining can be performed without violating privacy. In this paper, we first provide an overview of privacy preserving data mining focusing on distributed data sources, then we compare two technologies used in privacy preserving data mining. The first technology is encryptionbased, and it is used in earlier approaches. The second technology is secret-sharing which is recently being considered as a more efficient approach.

Fuzzy vault is a well-known technique to address the privacy concerns in biometric identification applications. We revisit the fuzzy vault scheme to address implementation, efficiency, and security issues encountered in its realization. We use the fingerprint data as a case study. We compare the performances of two different methods used in the implementation of fuzzy vault, namely brute force and Reed Solomon decoding. We show that the locations of fake (chaff) points in the vault leak information on the genuine points and propose a new chaff point placement technique that makes distinguishing genuine points impossible. We also propose a novel method for creation of chaff points that decreases the success rate of the brute force attack from 100% to less than 3.5%. While this paper lays out a complete guideline as to how the fuzzy vault is implemented in an efficient and secure way, it also points out that more research is needed to thwart the proposed attacks by presenting ideas for future research.

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 noncolluding 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 out-performs the others in execution time and communication overhead on data holders, but also uses a more efficient model for many data mining applications.

Location and time information about individuals can be captured through GPS devices, GSM phones, RFID tag readers, and by other similar means. Such data can be pre-processed to obtain trajectories which are sequences of spatio-temporal data points belonging to a moving object. Recently, advanced data mining techniques have been developed for extracting patterns from moving object trajectories to enable applications such as city traffic planning, identification of evacuation routes, trend detection, and many more. However, when special care is not taken, trajectories of individuals may also pose serious privacy risks even after they are de-identified or mapped into other forms. In this paper, we show that an unknown private trajectory can be re-constructed from knowledge of its properties released for data mining, which at first glance may not seem to pose any privacy threats. In particular, we propose a technique to demonstrate how private trajectories can be re-constructed from knowledge of their distances to a bounded set of known trajectories. Experiments performed on real data sets show that the number of known samples is surprisingly smaller than the actual theoretical bounds.Keywords: Privacy, Spatio-temporal data, trajectories, data mining.