Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network

Personalized recommender systems rely on each user's personal usage data in the system, in order to assist in decision making. However, privacy policies protecting users' rights prevent these highly personal data from being publicly available to a wider researcher audience. In this work, we propose a memory biased random walk model on multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the synthetic data in training recommender system models for cases when privacy policies restrict clickstream publishing.Comment: The new updated version of the pape

Supporting Regularized Logistic Regression Privately and Efficiently

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Increasing concerns over data privacy make it more and more difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used machine learning model in various disciplines while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluation on several studies validated the privacy guarantees, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc

Detection and Privacy Preservation of Sensitive Attributes Using Hybrid Approach for Privacy Preserving Record Linkage

Privacy Preserving Record Linkage (PPRL) is a major area of database research which entangles in colluding huge multiple heterogeneous data sets with disjunctive or additional information about an entity while veiling its private information. This paper gives an enhanced algorithm for merging two datasets using Sorted Neighborhood Deterministic approach and an improved Preservation algorithm which makes use of automatic selection of sensitive attributes and pattern mining over dynamic queries. We guarantee strong privacy, less computational complexity and scalability and address the legitimate concerns over data security and privacy with our approach

Conclave: secure multi-party computation on big data (extended TR)

Secure Multi-Party Computation (MPC) allows mutually distrusting parties to run joint computations without revealing private data. Current MPC algorithms scale poorly with data size, which makes MPC on "big data" prohibitively slow and inhibits its practical use. Many relational analytics queries can maintain MPC's end-to-end security guarantee without using cryptographic MPC techniques for all operations. Conclave is a query compiler that accelerates such queries by transforming them into a combination of data-parallel, local cleartext processing and small MPC steps. When parties trust others with specific subsets of the data, Conclave applies new hybrid MPC-cleartext protocols to run additional steps outside of MPC and improve scalability further. Our Conclave prototype generates code for cleartext processing in Python and Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave scales to data sets between three and six orders of magnitude larger than state-of-the-art MPC frameworks support on their own. Thanks to its hybrid protocols, Conclave also substantially outperforms SMCQL, the most similar existing system.Comment: Extended technical report for EuroSys 2019 pape

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

Exploring Hybrid Parallel Systems for Probabilistic Record Linkage

[EN] Record linkage is a technique widely used to gather data stored in disparate data sources that presumably pertain to the same real world entity. This integration can be done deterministically or probabilistically, depending on the existence of common key attributes among all data sources involved. The probabilistic approach is very time-consuming due to the amount of records that must be compared, specifically in big data scenarios. In this paper, we propose and evaluate a methodology that simultaneously exploits multicore and multi-GPU architectures in order to perform the probabilistic linkage of large-scale Brazilian governmental databases. We present some algorithmic optimizations that provide high accuracy and improve performance by defining the best algorithm-architecture combination for a problem given its input size. We also discuss performance results obtained with different data samples, showing that a hybrid approach outperforms other configurations, providing an average speedup of 7.9 when linking up to 20.000 million records.This work has been partially supported by CNPq, FAPESB, Bill & Melinda Gates Foundation, The Royal Society (UK), Medical Research Council (UK), NVIDIA Hardware Grant Program, Generalitat Valenciana (Grant PROMETEOII/2014/003), Spanish Government and European Commission through TEC2015-67387-C4-1-R (MINECO/FEDER), and network CAPAP-H. We have also worked in cooperation with the EU-COST Programme Action IC1305, "Network for Sustainable Ultrascale Computing (NESUS)Boratto, M.; Alonso-Jordá, P.; Pinto, C.; Melo, P.; Barreto, M.; Denaxas, S. (2019). Exploring Hybrid Parallel Systems for Probabilistic Record Linkage. The Journal of Supercomputing. 75:1137-1149. https://doi.org/10.1007/s11227-018-2328-3S1137114975Andrade G, Viegas F, Ramos GS, Almeida J, Rocha L, Gonçalves M, Ferreira R (2013) GPU-NB: a fast CUDA-based implementation of Naïve Bayes. In: 2013 25th International Symposium on Computer Architecture and High Performance Computing, pp 168–175Bloom BH (1970) Space/time trade-offs in hash coding with allowable errors. Commun ACM 13(7):422–426Cook S (2013) CUDA Programming: A Developer’s Guide to Parallel Computing with GPUs, 1st edn. Morgan Kaufmann, San FranciscoDoan A, Halevy A, Ives Z (2012) Principles of Data Integration. Elsevier, AmsterdamÉtienne EY (2012) Hyper-threading. TurbsPublishing, SaarbrückenFellegi IP, Sunter AB (1969) A theory for record linkage. J Am Stat Assoc 64:1183–1210Feng X, Jin H, Zheng R, Zhu L (2014) Near-duplicate detection using GPU-based simhash scheme. In: 2014 International Conference on Smart Computing, pp 223–228Forchhammer B, Papenbrock T, Stening T, Viehmeier S, Naumann U.D.F (2013) Duplicate detection on GPUs. In: BTW. Köllen-Verlag, pp 165–184Kim H.s, Lee D (2007) Parallel linkage. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, CIKM 2007. ACM, New York, NY, USA, pp 283–292Mamun AA, Aseltine R, Rajasekaran S (2015) RLT-S: a web system for record linkage. PLoS ONE 10(5):1–9Mamun AA, Aseltine R, Rajasekaran S (2016) Efficient record linkage algorithms using complete linkage clustering. PLoS ONE 11(4):1–21Mamun AA, Mi T, Aseltine R, Rajasekaran S (2014) Efficient sequential and parallel algorithms for record linkage. J Am Med Inform Assoc 21(2):252–262Mizell E, Biery R (2017) How GPUs are defining the future of data analyticsMunshi A, Gaster B, Mattson TG, Fung J, Ginsburg D (2011) OpenCL Programming Guide, 1st edn. Addison-Wesley, ReadingNVIDIA Corporation: NVIDIA CUDA C programming guide (2010). Version 3.2OpenMP Architecture Review Board: OpenMP application program interface version 4.0 (2013)Pokorny J (2011) NoSQL databases: a step to database scalability in web environment. In: Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services, iiWAS ’11. ACM, New York, NY, USA, pp 278–283Rendle S, Schmidt-Thieme L (2008) Scaling Record Linkage to Non-uniform Distributed Class Sizes. Springer, Berlin, pp 308–319Sehili Z, Kolb L, Borgs C, Schnell R, Rahm E (2015) Privacy preserving record linkage with ppjoin. In: Datenbanksysteme für Business, Technologie und Web (BTW), pp 85–104Winkler WE (1999) The state of record linkage and current research problemsZhong Z, Rychkov V, Lastovetsky A (2015) Data partitioning on multicore and multi-GPU platforms using functional performance models. IEEE Trans Comput 64(9):2506–251