TY  - JOUR
A1  - Caruccio, Loredana
A1  - Deufemia, Vincenzo
A1  - Naumann, Felix
A1  - Polese, Giuseppe
T1  - Discovering relaxed functional dependencies based on multi-attribute dominance
JF  - IEEE transactions on knowledge and data engineering
N2  - With the advent of big data and data lakes, data are often integrated from multiple sources. Such integrated data are often of poor quality, due to inconsistencies, errors, and so forth. One way to check the quality of data is to infer functional dependencies (fds). However, in many modern applications it might be necessary to extract properties and relationships that are not captured through fds, due to the necessity to admit exceptions, or to consider similarity rather than equality of data values. Relaxed fds (rfds) have been introduced to meet these needs, but their discovery from data adds further complexity to an already complex problem, also due to the necessity of specifying similarity and validity thresholds. We propose Domino, a new discovery algorithm for rfds that exploits the concept of dominance in order to derive similarity thresholds of attribute values while inferring rfds. An experimental evaluation on real datasets demonstrates the discovery performance and the effectiveness of the proposed algorithm.
KW  - Complexity theory
KW  - Approximation algorithms
KW  - Big Data
KW  - Distributed
KW  - databases
KW  - Semantics
KW  - Lakes
KW  - Functional dependencies
KW  - data profiling
KW  - data cleansing
Y1  - 2020
U6  - https://doi.org/10.1109/TKDE.2020.2967722
SN  - 1041-4347
SN  - 1558-2191
VL  - 33
IS  - 9
SP  - 3212
EP  - 3228
PB  - Institute of Electrical and Electronics Engineers
CY  - New York, NY
ER  - 
TY  - JOUR
A1  - Ambassa, Pacome L.
A1  - Kayem, Anne Voluntas dei Massah
A1  - Wolthusen, Stephen D.
A1  - Meinel, Christoph
T1  - Inferring private user behaviour based on information leakage
JF  - Smart Micro-Grid Systems Security and Privacy
N2  - In rural/remote areas, resource constrained smart micro-grid (RCSMG) architectures can provide a cost-effective power supply alternative in cases when connectivity to the national power grid is impeded by factors such as load shedding. RCSMG architectures can be designed to handle communications over a distributed lossy network in order to minimise operation costs. However, due to the unreliable nature of lossy networks communication data can be distorted by noise additions that alter the veracity of the data. In this chapter, we consider cases in which an adversary who is internal to the RCSMG, deliberately distorts communicated data to gain an unfair advantage over the RCSMG’s users. The adversary’s goal is to mask malicious data manipulations as distortions due to additive noise due to communication channel unreliability. Distinguishing malicious data distortions from benign distortions is important in ensuring trustworthiness of the RCSMG. Perturbation data anonymisation algorithms can be used to alter transmitted data to ensure that adversarial manipulation of the data reveals no information that the adversary can take advantage of. However, because existing data perturbation anonymisation algorithms operate by using additive noise to anonymise data, using these algorithms in the RCSMG context is challenging. This is due to the fact that distinguishing benign noise additions from malicious noise additions is a difficult problem. In this chapter, we present a brief survey of cases of privacy violations due to inferences drawn from observed power consumption patterns in RCSMGs centred on inference, and propose a method of mitigating these risks. The lesson here is that while RCSMGs give users more control over power management and distribution, good anonymisation is essential to protecting personal information on RCSMGs.
KW  - Approximation algorithms
KW  - Electrical products
KW  - Home appliances
KW  - Load modeling
KW  - Monitoring
KW  - Power demand
KW  - Wireless sensor networks
KW  - Distributed snapshot algorithm
KW  - Micro-grid networks
KW  - Power consumption characterization
KW  - Sensor networks
Y1  - 2018
SN  - 978-3-319-91427-5
SN  - 978-3-319-91426-8
U6  - https://doi.org/10.1007/978-3-319-91427-5_7
VL  - 71
SP  - 145
EP  - 159
PB  - Springer
CY  - Dordrecht
ER  -