Sharif Digital Repository

Today, many applications produce and use time series data. The data of this type may contain sensitive information. So they should be protected against unauthorized accesses. In this paper, security issues of time series data are identified and an access and inference control model for satisfying the identified security requirements is proposed. Using this model, administrators can define authorization rules based on various time-based granularities (e.g. day or month) and apply value-based constraints over the accessed times series data. Furthermore, they can define policy rules over the composition of multiple time-series other than the base time-series data. Detecting and resolving... 

Internet-of-Things (IoT) devices and applications are being deployed in our homes and workplaces. These devices often rely on continuous data collection to feed machine learning models. However, this approach introduces several privacy and efficiency challenges, as the service operator can perform unwanted inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger and more complicated models. In this article, we present a hybrid approach for breaking down large, complex deep neural networks for cooperative, and... 

We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model which is trained and evaluated based on information theoretic constraints. Using the selective exchange of information between a user's device and a service provider, DPFE enables the user to prevent certain sensitive information from being shared with a service provider, while allowing them to extract approved information using their model. We introduce and utilize the log-rank privacy, a novel measure to assess the effectiveness of DPFE in removing sensitive information and compare different models based on their accuracy-privacy trade-off. We then implement and evaluate the performance of DPFE on smartphones to... 

This paper presents an extension to PathFinder FPGA routing algorithm, which enables it to deliver FPGA designs free from risks of crosstalk attacks. Crosstalk side-channel attacks are a real threat in large designs assembled from various IPs, where some IPs are provided by trusted and some by untrusted sources. It suffices that a ring-oscillator based sensor is conveniently routed next to a signal that carries secret information (for instance, a cryptographic key), for this information to possibly get leaked. To address this security concern, we apply several different strategies and evaluate them on benchmark circuits from Verilog-to-Routing tool suite. Our experiments show that, for a... 

We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model which is trained and evaluated based on information theoretic constraints. Using the selective exchange of information between a user's device and a service provider, DPFE enables the user to prevent certain sensitive information from being shared with a service provider, while allowing them to extract approved information using their model. We introduce and utilize the log-rank privacy, a novel measure to assess the effectiveness of DPFE in removing sensitive information and compare different models based on their accuracy-privacy trade-off. We then implement and evaluate the performance of DPFE on smartphones to... 

In an increasing competitive world, marketing survival can be depended simply on timely new information on customers and market trend. One of the most important strategies in CRM (Customer Relationship Management) is to capture enough information from customers and using this information carefully [Ryals, Tinsley]. Of course security of this information is very important in CRM data management [Bryan]. Data management is a method for scheduling and controlling data saving, recovering and processing. This activity has been done continually or periodically[Bryan]. Security level of this information depends on the security policy of the organization. CRM security policy is the directives and... 

Connected and Autonomous Vehicles (CAVs) are introduced to improve individuals' quality of life by offering a wide range of services. They collect a huge amount of data and exchange them with each other and the infrastructure. The collected data usually includes sensitive information about the users and the surrounding environment. Therefore, data security and privacy are among the main challenges in this industry. Blockchain, an emerging distributed ledger, has been considered by the research community as a potential solution for enhancing data security, integrity, and transparency in Intelligent Transportation Systems (ITS). However, despite the emphasis of governments on the transparency...