p-probabilistic k-anonymous microaggregation for the anonymization of surveys with uncertain participation

We develop a probabilistic variant of k-anonymous microaggregation which we term p-probabilistic resorting to a statistical model of respondent participation in order to aggregate quasi-identifiers in such a manner that k-anonymity is concordantly enforced with a parametric probabilistic guarantee. Succinctly owing the possibility that some respondents may not finally participate, sufficiently larger cells are created striving to satisfy k-anonymity with probability at least p. The microaggregation function is designed before the respondents submit their confidential data. More precisely, a specification of the function is sent to them which they may verify and apply to their quasi-identifying demographic variables prior to submitting the microaggregated data along with the confidential attributes to an authorized repository. We propose a number of metrics to assess the performance of our probabilistic approach in terms of anonymity and distortion which we proceed to investigate theoretically in depth and empirically with synthetic and standardized data. We stress that in addition to constituting a functional extension of traditional microaggregation, thereby broadening its applicability to the anonymization of statistical databases in a wide variety of contexts, the relaxation of trust assumptions is arguably expected to have a considerable impact on user acceptance and ultimately on data utility through mere availability.Peer ReviewedPostprint (author's final draft

DataSHIELD – new directions and dimensions

In disciplines such as biomedicine and social sciences, sharing and combining sensitive individual-level data is often prohibited by ethical-legal or governance constraints and other barriers such as the control of intellectual property or the huge sample sizes. DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonised Individual-levEL Databases) is a distributed approach that allows the analysis of sensitive individual-level data from one study, and the co-analysis of such data from several studies simultaneously without physically pooling them or disclosing any data. Following initial proof of principle, a stable DataSHIELD platform has now been implemented in a number of epidemiological consortia. This paper reports three new applications of DataSHIELD including application to post-publication sensitive data analysis, text data analysis and privacy protected data visualisation. Expansion of DataSHIELD analytic functionality and application to additional data types demonstrate the broad applications of the software beyond biomedical sciences

Efficient Dynamic Searchable Symmetric Encryption Over Medical Cloud Data

Many web computing systems are running constant database services where their data change consistently and grow incrementally. In this unique circumstance, web data services have a noteworthy part and attract huge changes observing and controlling the data honesty and data spread. At present, web telemedicine database services are of focal significance to distributed systems. Be that as it may, the expanding many-sided quality and the fast development of this present reality social insurance testing applications make it difficult to instigate the database authoritative staff. The proposed approach is approved inside by measuring the effect of utilizing our computing services systems on different execution highlights like interchanges cost, reaction time, and throughput. The outcomes demonstrate that our incorporated approach essentially enhances the execution of web database systems and beats its partners. The strategies for workload-mindful anonymization for determination predicates have been examined in the writing. Notwithstanding, to the best of our insight, the issue of fulfilling the exactness limitations for different parts has not been examined some time recently. In our detailing of the previously mentioned issue, we propose heuristics for anonymization calculations and show observationally that the proposed approach fulfills imprecision limits for a bigger number of consents and has bring down aggregate imprecision than the present cutting edge and Fully Authenticated towards aggressor and data recovery