Quantifying Privacy Loss of Human Mobility Graph Topology

Human mobility is often represented as a mobility network, or graph, with nodes representing places of significance which an individual visits, such as their home, work, places of social amenity, etc., and edge weights corresponding to probability estimates of movements between these places. Previous research has shown that individuals can be identified by a small number of geolocated nodes in their mobility network, rendering mobility trace anonymization a hard task. In this paper we build on prior work and demonstrate that even when all location and timestamp information is removed from nodes, the graph topology of an individual mobility network itself is often uniquely identifying. Further, we observe that a mobility network is often unique, even when only a small number of the most popular nodes and edges are considered. We evaluate our approach using a large dataset of cell-tower location traces from 1 500 smartphone handsets with a mean duration of 430 days. We process the data to derive the top−N places visited by the device in the trace, and find that 93% of traces have a unique top−10 mobility network, and all traces are unique when considering top−15 mobility networks. Since mobility patterns, and therefore mobility networks for an individual, vary over time, we use graph kernel distance functions, to determine whether two mobility networks, taken at different points in time, represent the same individual. We then show that our distance metrics, while imperfect predictors, perform significantly better than a random strategy and therefore our approach represents a significant loss in privacy

Poly(adenosine diphosphate-ribose) polymerase 1 expression in malignant melanomas from photoexposed areas of the head and neck region.

Summary The family of the poly(adenosine diphosphate-ribose) polymerase (PARP) proteins is directly involved in genomic stability, DNA repair, and apoptosis by DNA damage. In this study, we evaluated the role of PARP-1 in melanoma and its prognostic importance. We studied by immunohistochemistry and Western blot analysis PARP-1 expression in a selected series of 80 primary melanoma of the head and neck region. The results were correlated with tumor thickness and patient’s outcome. A follow-up of at least 3 years was available. Fifteen cases of benign melanocytic nevi were used as controls. Normal melanocytes showed only scattered, focal nuclear positivity and were considered as negative for PARP-1 expression by immunohistochemistry (score, 0). Thirty cases of melanoma (37.5%) showed nuclear expression of PARP-1 in both radial and vertical growth phases. Western blot analysis showed the presence of a high signal for full-length PARP-1 only in the cases with high immunohistochemical (nuclear) expression of protein (score, ++/+++) in both radial and vertical growth phase. A significant correlation was present between PARP-1 expression in vertical growth phase and the thickness of tumor lesion ( P = .014); all but one tumor measuring less than 0.75 mm showed no or low PARP-1 expression. No correlation was found between PARP-1 expression in radial growth phase and tumor thickness ( P = .38, data not shown). These data suggest that PARP-1 overexpression is a potential novel molecular marker of aggressive cutaneous malignant melanoma and a direct correlation between PARP-1–mediated inhibition of the apoptosis and biologic behavior of cutaneous malignant melanoma

Omics-based molecular techniques in oral pathology centred cancer: Prospect and challenges in Africa

: The completion of the human genome project and the accomplished milestones in the human proteome project; as well as the progress made so far in computational bioinformatics and “big data” processing have contributed immensely to individualized/personalized medicine in the developed world.At the dawn of precision medicine, various omics-based therapies and bioengineering can now be applied accurately for the diagnosis, prognosis, treatment, and risk stratifcation of cancer in a manner that was hitherto not thought possible. The widespread introduction of genomics and other omics-based approaches into the postgraduate training curriculum of diverse medical and dental specialties, including pathology has improved the profciency of practitioners in the use of novel molecular signatures in patient management. In addition, intricate details about disease disparity among diferent human populations are beginning to emerge. This would facilitate the use of tailor-made novel theranostic methods based on emerging molecular evidences

Quantifying Privacy Loss of Human Mobility Graph Topology.

network, or graph, with nodes representing places of significance which an individual visits, such as their home, work, places of social amenity, etc., and edge weights corresponding to probability estimates of movements between these places. Previous research has shown that individuals can be identified by a small number of geolocated nodes in their mobility network, rendering mobility trace anonymization a hard task. In this paper we build on prior work and demonstrate that even when all location and timestamp information is removed from nodes, the graph topology of an individual mobility network itself is often uniquely identifying. Further, we observe that a mobility network is often unique, even when only a small number of the most popular nodes and edges are considered. We evaluate our approach using a large dataset of cell-tower location traces from 1 500 smartphone handsets with a mean duration of 430 days. We process the data to derive the top−N places visited by the device in the trace, and find that 93% of traces have a unique top−10 mobility network, and all traces are unique when considering top−15 mobility networks. Since mobility patterns, and therefore mobility networks for an individual, vary over time, we use graph kernel distance functions, to determine whether two mobility networks, taken at different points in time, represent the same individual. We then show that our distance metrics, while imperfect predictors, perform significantly better than a random strategy and therefore our approach represents a significant loss in privacy