A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks

<div><p>Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter <i>K</i> value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter <i>K</i> value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules.</p></div

La biofisica

Un vasto progetto che, prendendo le mosse da una nuova teoria del mondo della vita, dà nuove interpretazioni dell'uomo, del suo vivere, del suo pensare

Sequential rules mined from simulated anonymity datasets for LBS continuous queries.

<p>Sequential rules mined from simulated anonymity datasets for LBS continuous queries.</p

Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset

<div><p>Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users’ privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction methods based on transition probability matrices constructing from sequential rules for spatial-temporal k-anonymity dataset. First, we define single-step sequential rules mined from sequential spatial-temporal k-anonymity datasets generated from continuous LBS queries for multiple users. We then construct transition probability matrices from mined single-step sequential rules, and normalize the transition probabilities in the transition matrices. Next, we regard a mobility model for an LBS requester as a stationary stochastic process and compute the n-step transition probability matrices by raising the normalized transition probability matrices to the power n. Furthermore, we propose two location prediction methods: rough prediction and accurate prediction. The former achieves the probabilities of arriving at target locations along simple paths those include only current locations, target locations and transition steps. By iteratively combining the probabilities for simple paths with n steps and the probabilities for detailed paths with n-1 steps, the latter method calculates transition probabilities for detailed paths with n steps from current locations to target locations. Finally, we conduct extensive experiments, and correctness and flexibility of our proposed algorithm have been verified.</p></div

Sei pezzi meno facili: relatività einsteiniana, simmetria, spazio-tempo

Una scelta di testi "meno facili" operata tra le "Lectures on Physics" di Feynman. Filo conduttore di questo volume è una teoria tanto popolare quanto poco compresa: la teoria della relatività di Einstein. Come disse il fisico Freeman Dyson, che fu suo allievo al Caltech, in Feynman "il pensiero profondo e il fare burlesco e giocoso non erano parti separate di una personalità divisa... egli faceva le due cose contemporaneamente". Seguire una di queste lezioni richiede una costante attenzione ai trabocchetti che il fisico tende di continuo, avvalendosi di uno stile dialogico degno di un filosofo antico. In Feynman nessun concetto è così ovvio o elementare da non meritare un supplemento di indagine, un'analisi più attenta

Non-sensitive rules variation ratios change with data expansion using the <i>NOSTK</i> method.

<p>Non-sensitive rules variation ratios change with data expansion using the <i>NOSTK</i> method.</p

Hiding sensitive rules ratios change with data expansion using the <i>NOSTK</i> method.

<p>Hiding sensitive rules ratios change with data expansion using the <i>NOSTK</i> method.</p

Newly generated sensitive rules ratios change with <i>K</i> values for different incremental combinations.

<p>Newly generated sensitive rules ratios change with <i>K</i> values for different incremental combinations.</p

Hiding sensitive rules ratios change with <i>K</i> values for different incremental combinations.

<p>Hiding sensitive rules ratios change with <i>K</i> values for different incremental combinations.</p

Non-sensitive rules variation ratios difference between the <i>NOSTK</i> method and the <i>tSTK</i> method.

<p>Non-sensitive rules variation ratios difference between the <i>NOSTK</i> method and the <i>tSTK</i> method.</p