Rewriting Complex Queries from Cloud to Fog under Capability Constraints to Protect the Users' Privacy

In this paper we show how existing query rewriting and query containment techniques can be used to achieve an efficient and privacy-aware processing of queries. To achieve this, the whole network structure, from data producing sensors up to cloud computers, is utilized to create a database machine consisting of billions of devices from the Internet of Things. Based on previous research in the field of database theory, especially query rewriting, we present a concept to split a query into fragment and remainder queries. Fragment queries can operate on resource limited devices to filter and preaggregate data. Remainder queries take these data and execute the last, complex part of the original queries on more powerful devices. As a result, less data is processed and forwarded in the network and the privacy principle of data minimization is accomplished

The Internet of Things as a Privacy-Aware Database Machine

Instead of using a computer cluster with homogeneous nodes and very fast high bandwidth connections, we want to present the vision to use the Internet of Things (IoT) as a database machine. This is among others a key factor for smart (assistive) systems in apartments (AAL, ambient assisted living), offices (AAW, ambient assisted working), Smart Cities as well as factories (IIoT, Industry 4.0). It is important to massively distribute the calculation of analysis results on sensor nodes and other low-resource appliances in the environment, not only for reasons of performance, but also for reasons of privacy and protection of corporate knowledge. Thus, functions crucial for assistive systems, such as situation, activity, and intention recognition, are to be automatically transformed not only in database queries, but also in local nodes of lower performance. From a database-specific perspective, analysis operations on large quantities of distributed sensor data, currently based on classical big-data techniques and executed on large, homogeneously equipped parallel computers have to be automatically transformed to billions of processors with energy and capacity restrictions. In this visionary paper, we will focus on the database-specific perspective and the fundamental research questions in the underlying database theory

Query Rewriting by Contract under Privacy Constraints

In this paper we show how Query Rewriting rules and Containment checks of aggregate queries can be combined with Contract-based programming techniques. Based on the combination of both worlds, we are able to find new Query Rewriting rules for queries containing aggregate constraints. These rules can either be used to improve the overall system performance or, in our use case, to implement a privacy-aware way to process queries. By integrating them in our PArADISE framework, we can now process and rewrite all types of OLAP queries, including complex aggregate functions and group-by extensions. In our framework, we use the whole network structure, from data producing sensors up to cloud computers, to automatically deploy an edge computing subnetwork. On each edge node, so-called fragment queries of a genuine query are executed to filter and to aggregate data on resource restricted sensor nodes. As a result of integrating Contract-based programming approaches, we are now able to not only process less data but also to produce less data in the result. Thus, the privacy principle of data minimization is accomplished

Privacy Protection through Query Rewriting in Smart Environments

By the events in the past years, the integration of data protection mechanisms into information systems becomes a central research problem again. In this technical report for our poster, we show how query rewriting can be used to maintain privacy of users in smart (or assistive) environments. We developed a privacy respecting query processing and a vertical fragmentation of queries, processing maximal parts of the query as close to the sources of the data (e.g. sensors) as possible

The Internet of Things as a Privacy-Aware Database Machine

Instead of using a computer cluster with homogeneous nodes and very fast high bandwidth connections, we want to present the vision to use the Internet of Things (IoT) as a database machine. This is among others a key factor for smart (assistive) systems in apartments (AAL, ambient assisted living), offices (AAW, ambient assisted working), Smart Cities as well as factories (IIoT, Industry 4.0). It is important to massively distribute the calculation of analysis results on sensor nodes and other low-resource appliances in the environment, not only for reasons of performance, but also for reasons of privacy and protection of corporate knowledge. Thus, functions crucial for assistive systems, such as situation, activity, and intention recognition, are to be automatically transformed not only in database queries, but also in local nodes of lower performance. From a database-specific perspective, analysis operations on large quantities of distributed sensor data, currently based on classical big-data techniques and executed on large, homogeneously equipped parallel computers have to be automatically transformed to billions of processors with energy and capacity restrictions. In this visionary paper, we will focus on the database-specific perspective and the fundamental research questions in the underlying database theory

Query Rewriting by Contract under Privacy Constraints

In this paper we show how Query Rewriting rules and Containment checks of aggregate queries can be combined with Contract-based programming techniques. Based on the combination of both worlds, we are able to find new Query Rewriting rules for queries containing aggregate constraints. These rules can either be used to improve the overall system performance or, in our use case, to implement a privacy-aware way to process queries. By integrating them in our PArADISE framework, we can now process and rewrite all types of OLAP queries, including complex aggregate functions and group-by extensions. In our framework, we use the whole network structure, from data producing sensors up to cloud computers, to automatically deploy an edge computing subnetwork. On each edge node, so-called fragment queries of a genuine query are executed to filter and to aggregate data on resource restricted sensor nodes. As a result of integrating Contract-based programming approaches, we are now able to not only process less data but also to produce less data in the result. Thus, the privacy principle of data minimization is accomplished

Rewriting Complex Queries from Cloud to Fog under Capability Constraints to Protect the Users' Privacy

In this paper we show how existing query rewriting and query containment techniques can be used to achieve an efficient and privacy-aware processing of queries. To achieve this, the whole network structure, from data producing sensors up to cloud computers, is utilized to create a database machine consisting of billions of devices from the Internet of Things. Based on previous research in the field of database theory, especially query rewriting, we present a concept to split a query into fragment and remainder queries. Fragment queries can operate on resource limited devices to filter and preaggregate data. Remainder queries take these data and execute the last, complex part of the original queries on more powerful devices. As a result, less data is processed and forwarded in the network and the privacy principle of data minimization is accomplished

Immunomodulator comedication promotes the reversal of anti-drug antibody-mediated loss of response to anti-TNF therapy in inflammatory bowel disease

Purpose: Loss of therapeutic response (LOR) due to anti-drug antibodies (ADA) against tumor necrosis factor (TNF) inhibitors is common in patients with inflammatory bowel disease (IBD). We aimed to investigate whether immunomodulator comedication can reverse the immunogenic LOR to TNF inhibitors in IBD. Methods: In this real-world retrospective cohort study, 123 IBD patients with neutralizing ADA to infliximab or adalimumab and concomitant subtherapeutic trough levels were screened for clinical LOR. Subsequent ADA and trough level measurements and clinical outcomes were analyzed for patients who received either immunomodulator comedication or dose intensification of infliximab or adalimumab to overcome LOR. Results: Following immunogenic LOR, the initial anti-TNF regimen was optimized in 33 patients. In univariable and multivariable logistic regression analyses, immunomodulator comedication was identified as the crucial factor for regaining clinical remission and ADA clearance. Detectable trough levels (≥ 0.98 or ≥ 1.00 mg/L, respectively) had optimal predictive performance for both endpoints in receiver operating characteristics curves [area under the curve 0.86 (95% confidence interval 0.68–1.00) for regaining clinical remission, 0.87 (0.71–1.00) for ADA clearance]. Furthermore, 11/20 patients (55%) on a comedication with azathioprine or methotrexate and 2/13 patients (15%) receiving anti-TNF dose intensification exclusively ( P  = 0.032) exhibited ADA elimination, regain of therapeutic trough levels, and clinical remission. Regain of clinical remission alone was achieved in 17/20 (85%) patients receiving comedication and 2/13 (15%) patients receiving anti-TNF dose intensification ( P  = 1.6 × 10 −4 ). Conclusion: Immunogenic LOR to infliximab or adalimumab in IBD can be successfully reversed using immunomodulator comedication