A RESEARCH SURVEY: RFID SECURITY & PRIVACY ISSUE <s> Privacy Threats Phases & Preserving Techniques <s> The Auto-ID Center is developing low-cost radio frequency identification (RFID) based systems with the initial application as next generation bar-codes. We describe RFID technology, summarize our approach and our research, and most importantly, describe the research opportunities in RFID for experts in cryptography and information security. The common theme in low-cost RFID systems is that computation resources are very limited, and all aspects of the RFID system are connected to each other. Understanding these connections and the resulting design trade-offs is an important prerequisite to effectively answering the challenges of security and privacy in low-cost RFID systems. <s> BIB001 </s> A RESEARCH SURVEY: RFID SECURITY & PRIVACY ISSUE <s> Privacy Threats Phases & Preserving Techniques <s> Radio Frequency IDentification (RFID), a technology for automatic object identification, has wide applications in many areas including manufacturing, healthcare, and transportation. Yet, the uniquely identifiable objects pose a privacy threat to individuals carrying the objects. Most previous work on privacy-preserving RFID technology, such as EPC re-encryption and killing tags, focused on the threats caused by the physical RFID tags in the data collection phase, but these techniques cannot address the privacy threats in the data publishing phase, when a large volume of RFID data is released to a third party. In this paper, we study the privacy threats caused by publishing RFID data. Even if the explicit identifying information, such as name and social security number, has been removed from the published RFID data, an adversary may identify a target victim's record or infer her sensitive value by matching a priori known visited locations and timestamps. RFID data by default is high-dimensional, so applying traditional anonymity model to RFID data suffers from the curse of high dimensionality, and would result in poor data usefulness. We define a new privacy model, develop an anonymization algorithm to address the special challenges on RFID data, and evaluate its performance in terms of data quality and efficiency. <s> BIB002
There are two phases of data privacy threats in terms of physical RFID tags:- Many privacy preserving technology BIB001 like EPC re-encryption and killing tags [9] addresses the privacy threat in the data collection phase whereas anonymization algorithms works on the privacy threat in the data publishing phase BIB002 . The anonymization algorithm for the query engine transforms the raw object-specific RFID data into a version that is immunized against privacy attacks. Many privacy models such as K-anonymity, L-diversity, T-closeness has been proposed to thwart privacy threats caused by the record linkage and attributes linkage attacks in the context of relational databases. All these models works assuming that a given set of attributes called quasiidentifier (QID) can identify an individual. However these models are effective for anonymization on relational databases but these models are not applicable to RFID data (due to high dimensionality nature of RFID data). RFID is high dimensional because of the large combinations of location and timestamp. For example, if a subway is having 100 stations that operate 20 hrs per day, then RFID table would have 100 X 20=2000 dimensions. Applying traditional privacy model, such as K-anonymity, would include all dimensions into a single QID and require every path to be shared by at least K records. However, in real-life privacy attacks, it is unlikely that an adversary could know all locations and timestamps that the target victim has visited because it requires non-trivial effort to gather each piece of prior knowledge from so many possible locations at different time. Thus, it is reasonable to assume that the adversary's prior knowledge is bounded by at most L pairs of locations and timestamps that the target victim is visited. Based on this LKC, privacy model is defined for anonymizing high-dimensional RFID data BIB002 but the limitation of this model is that the data holder must have advance knowledge of all the adversaries, which is impossible in reality. Most of the previous work on privacy preserving RFID technology focused on the threats caused by the physical RFID tags and proposed techniques like sleeping tags, and EPC re-encryption. Basically the privacy and security issue was at communication layer among tags and readers not on the database layer where a large amount of RFID data actually resides. The important part here is to preserve the data truthfulness, if data will be examined by human users for the purpose of auditing and data interpretation. BIB002 .