Students with location-aware of mobile devices are able to make queries about their surroundings at any time. computing paradigm brings great convenience for information access, it also raises concerns over potential intrusion into students location privacy. Cloud computing is an emerging computing paradigm in which resources of the computing infrastructure are provided as services over the Internet. To protect location privacy, one typical approach is to cloak students locations into spatial regions based on students-specified privacy requirements, and to transform location-based queries into region-based queries. In this paper, we identify and address three new issues concerning this location cloaking approach. To the best of our knowledge, this is the first work to explore the adaptation of the reputation management functions to changes in network conditions.
In recent years, both sophistication and damage potential of Internet worms have increased tremendously. To understand their threat, we need to look into their payload for signatures as well as propagation pattern for Internet-scale behavior. An accurate analytical propagation model allows us to comprehensively study how a worm propagates under various conditions, which is often computationally too intensive for simulations. More importantly, it gives us an insight into the impact of each worm/ network parameter on the propagation of the worm.
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DESCRIPTION
The integration of Global Positioning System (GPS) receivers and sensors into mobile devices has enabled collaborative sensing applications, which monitor the dynamics of environments through opportunistic collection of data from many students' devices. One example that motivates this paper is a probe-vehicle-based automotive traffic monitoring system, which estimates traffic congestion from GPS velocity measurements reported from many drivers.
This paper considers the problem of achieving guaranteed anonymity in a locational data set that includes location traces from many students, while maintaining high data accuracy. We consider two methods to reidentify anonymous location traces, target tracking, and home identification, and observe that known privacy algorithms cannot achieve high application accuracy requirements or fail to provide privacy guarantees for drivers in low-density areas.
To overcome these challenges, we derive a novel time-to-confusion criterion to characterize privacy in a locational data set and propose a disclosure control algorithm (called uncertainty-aware path cloaking algorithm) that selectively reveals GPS samples to limit the maximum time-to-confusion for all vehicles.
Through trace-driven simulations using real GPS traces from 312 vehicles, we demonstrate that this algorithm effectively limits tracking risks, in particular, by eliminating tracking outliers. It also achieves significant data accuracy improvements compared to known algorithms.
We then present two enhancements to the algorithm. First, it also addresses the home identification risk by reducing location information revealed at the start and end of trips. Second, it also considers heading information reported by students in the tracking model. This version can thus protect students who are moving in dense areas but in a different direction from the majority.
ACHIEVING SECURE, SCALABLE, AND FINE-GRAINED DATA ACCESS CONTROL IN CLOUD COMPUTING
DESCRIPTION
Cloud computing is an emerging computing paradigm in which resources of the computing infrastructure are provided as services over the Internet. As promising as it is, this paradigm also brings forth many new challenges for data security and access control when students outsource sensitive data for sharing on cloud servers, which are not within the same trusted domain as data owners.
To keep sensitive students data confidential against untrusted servers, existing solutions usually apply cryptographic methods by disclosing data decryption keys only to authorized students. However, in doing so, these solutions inevitably introduce a heavy computation overhead on the data owner for key distribution and data management when finegrained data access control is desired, and thus do not scale well. The problem of simultaneously achieving fine-grainednes, scalability, and data confidentiality of access control actually still remains unresolved.
This paper addresses this challenging open issue by, on one hand, defining and enforcing access policies based on data attributes, and, on the other hand, allowing the data owner to delegate most of the computation tasks involved in finegrained data access control to untrusted cloud servers without
disclosing the underlying data contents. We achieve this goal by exploiting and uniquely combining techniques of attribute-based encryption (ABE), proxy re-encryption, and lazy re-encryption.
Our proposed scheme also has salient properties of students access privilege confidentiality and students secret key accountability. Extensive analysis shows that our proposed scheme is highly efficient and provably secure under existing security models.
ADAPTATION OF REPUTATION MANAGEMENT SYSTEMS TO DYNAMIC NETWORK CONDITIONS IN AD HOC NETWORKS
DESCRIPTION
Reputation management systems have been proposed as a cooperation enforcement solution in ad hoc networks. Typically, the functions of reputation management (evaluation, detection, and reaction) are carried out homogeneously across time and space. However, the dynamic nature of ad hoc networks causes node behavior to vary both spatially and temporally due to changes in local and network-wide conditions.
When reputation management functions do not adapt to such changes, their effectiveness, measured in terms of accuracy (correct identification of node behavior) and promptness (timely identification of node misbehavior), may be compromised. We propose an adaptive reputation management system that realizes that changes in node behavior may be driven by changes in network conditions and that accommodates such changes by adapting its operating parameters.
We introduce a time-slotted approach to allow the evaluation function to quickly and accurately capture changes in node behavior. We show how the duration of an evaluation slot can adapt according to the network’s activity to enhance the system accuracy and promptness. We then show how the detection function can utilize a Sequential Probability Ratio Test (SPRT) to distinguish between cooperative and misbehaving neighbors.
The SPRT adapts to changes in neighbors’ behavior that are a by-product of changing network conditions, by using the node’s own behavior as a benchmark. We compare our proposed solution to a nonadaptive system, showing the ability of our system to achieve high accuracy and promptness in dynamic environments. To the best of our knowledge, this is the first work to explore the adaptation of the reputation management functions to changes in network conditions.
ADAPTIVE JOIN OPERATORS FOR RESULT RATE OPTIMIZATION ON STREAMING INPUTS
DESCRIPTION
Adaptive join algorithms have recently attracted a lot of attention in emerging applications where data are provided by autonomous data sources through heterogeneous network environments. Their main advantage over traditional join techniques is that they can start producing join results as soon as the first input tuples are available, thus, improving pipelining by smoothing join result production and by masking source or network delays.
In this paper, we first propose Double Index Nested-loops Reactive join (DINER), a new adaptive two-way join algorithm for result rate maximization. DINER combines two key elements: an intuitive flushing policy that aims to increase the productivity of in-memory tuples in producing results during the online phase of the join, and a novel reentrant join technique that allows the algorithm to rapidly switch between processing in-memory and disk-resident tuples, thus, better exploiting temporary delays when new data are not available.
We then extend the applicability of the proposed technique for a more challenging setup: handling more than two inputs. Multiple Index Nested-loop Reactive join (MINER) is a multiway join operator that inherits its principles from DINER.
Our experiments using real and synthetic data sets demonstrate that DINER outperforms previous adaptive join algorithms in producing result tuples at a significantly higher rate, while making better use of the available memory. Our experiments also shows that in the presence of multiple inputs, MINER manages to produce a high percentage of early results, outperforming existing techniques for adaptive multiway join.
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