Biblio

The existing anonymized differential privacy model adopts a unified anonymity method, ignoring the difference of personal privacy, which may lead to the problem of excessive or insufficient protection of the original data [1]. Therefore, this paper proposes a personalized k-anonymity model for tuples (PKA) and proposes a differential privacy data publishing algorithm (DPPA) based on personalized anonymity, firstly based on the tuple personality factor set by the user in the original data set. The values are classified and the corresponding privacy protection relevance is calculated. Then according to the tuple personality factor classification value, the data set is clustered by clustering method with different anonymity, and the quasi-identifier attribute of each cluster is aggregated and noise-added to realize anonymized differential privacy; finally merge the subset to get the data set that meets the release requirements. In this paper, the correctness of the algorithm is analyzed theoretically, and the feasibility and effectiveness of the proposed algorithm are verified by comparison with similar algorithms.

Due to the unavailability of signatures for previously unknown malware, non-signature malware detection schemes typically rely on analyzing program behavior. Prior behavior based non-signature malware detection schemes are either easily evadable by obfuscation or are very inefficient in terms of storage space and detection time. In this paper, we propose GZero, a graph theoretic approach fast and accurate non-signature malware detection at end hosts. GZero it is effective while being efficient in terms of both storage space and detection time. We conducted experiments on a large set of both benign software and malware. Our results show that GZero achieves more than 99% detection rate and a false positive rate of less than 1%, with less than 1 second of average scan time per program and is relatively robust to obfuscation attacks. Due to its low overheads, GZero can complement existing malware detection solutions at end hosts.

The rapid development of cloud computing has resulted in the emergence of numerous web services on the Internet. Selecting a suitable cloud service is becoming a major problem for users especially non-professionals. Quality of Service (QoS) is considered to be the criterion for judging web services. There are several Collaborative Filtering (CF)-based QoS prediction methods proposed in recent years. QoS values among different users may vary largely due to the network and geographical location. Moreover, QoS data provided by untrusted users will definitely affect the prediction accuracy. However, most existing methods seldom take both facts into consideration. In this paper, we present a trust-aware and location-based approach for web service QoS prediction. A trust value for each user is evaluated before the similarity calculation and the location is taken into account in similar neighbors selecting. A series of experiments are performed based on a realworld QoS dataset including 339 service users and 5,825 services. The experimental analysis shows that the accuracy of our method is much higher than other CF-based methods.