Deletion Schemes Research Articles

Empowering Data Owners: An Efficient and Verifiable Scheme for Secure Data Deletion

Cloud services have attracted numerous enterprises, organizations, and individual users due to their exceptional computing power and almost limitless storage capacity. A vast amount of business data and private data are continuously uploaded to the cloud platform, driven by a series of attractive services offered by the cloud. Unfortunately, once data is uploaded to the cloud, its owner has no way of ensuring that it is actually deleted as intended. This obviously increases the concerns of data owners about the security of their data, because it is related to the privacy of users. Therefore, there must be a reliable solution to prove that data is deleted as requested by users, to prevent data leakage or abuse. In existing data deletion schemes, most are designed based on cryptographic knowledge rather than erasure or overwrite techniques, in order not to cause incalculable damage to the storage medium. However, most cryptographic-based data deletion schemes, particularly attribute-based encryption, involve numerous complex bilinear mapping operations, which are expensive for most devices. To address this issue, the paper proposes an Efficient and Verifiable Scheme for Secure Data Deletion (EVSD). Firstly, Elliptic Curve Cryptography (ECC) is introduced to achieve efficient encryption of data. Then, leveraging Linear Secret Sharing Scheme (LSSS), fine-grained data deletion policies supporting logical operations are implemented. Finally, the deletion of the data is efficiently verified using the root of the Merkle Hash Tree (MHT) generated by the defined illegal and legal attributes, while the deletion proof is also generated. Satisfactorily, security analysis shows that the EVSD scheme is much more advantageous compared to existing schemes, and a trait likewise is also observed in the performance evaluation.

IoT security: On-chip secure deletion scheme using ECC modulation in IoT appliances

NAND flash memory-based IoT devices inherently suffer from data retention issues. In IoT security, these retention issues are significant and require a robust solution for secure deletion. Secure deletion methods can be categorized into off-chip and on-chip schemes. Off-chip secure deletion schemes, based on block-level erasure operations, are unable to perform real-time trim operations. Consequently, they are vulnerable to hacking threats. On the other hand, on-chip secure deletion schemes enable real-time trim operations by performing deletion on a page-by-page basis. However, the on-chip scheme introduces a challenge of program disturbance for neighboring page data. The proposed on-chip deletion scheme tackles this problem by utilizing ECC code modulation through a partial program operation. This approach significantly reduces the program disturbance issue associated with neighboring page data. Moreover, the proposed code modulation secure deletion scheme allows for real-time verification of the deletion of original data.

NASA: NVM-Assisted Secure Deletion for Flash Memory

Secure deletion in flash-based storage is crucial for data security. However, existing secure deletion schemes for flash memory suffer from performance degradation and reliability issues and cannot provide secure deletion guarantees. Although emerging nonvolatile memory (NVM) allows in-place updates and provides high performance, it is unable to fully replace flash memory and, thus, cannot solve the secure deletion problem. In this article, we propose NVM-assisted secure deletion scheme for flash memory (NASA), a stale-free storage system that combines NVM and flash memory to provide immediate secure deletion without significant performance degradation in SSDs. NASA uses block erasure to provide secure deletion guarantees for flash memory and exploits NVM to conceal time-consuming erasure operations. We demonstrate that unless the unique characteristics of NVM are considered, schemes that merely implement existing approaches to secure deletion will end up with stale data replicas within their storage media. Moreover, we evaluate NASA with different real-world workloads and demonstrate that NASA increases the average latency by 0.01% compared to LRU and decreases 2.1% average latency over the FIFO caching policy. NASA is a novel storage system that provides strong secure deletion guarantees with high performance.

Machine unlearning: linear filtration for logit-based classifiers

Recently enacted legislation grants individuals certain rights to decide in what fashion their personal data may be used and in particular a “right to be forgotten”. This poses a challenge to machine learning: how to proceed when an individual retracts permission to use data which has been part of the training process of a model? From this question emerges the field of machine unlearning, which could be broadly described as the investigation of how to “delete training data from models”. Our work complements this direction of research for the specific setting of class-wide deletion requests for classification models (e.g. deep neural networks). As a first step, we propose linear filtration as an intuitive, computationally efficient sanitization method. Our experiments demonstrate benefits in an adversarial setting over naive deletion schemes.

Edge computing empowered anomaly detection framework with dynamic insertion and deletion schemes on data streams

Anomaly detection plays a crucial role in many Internet of Things (IoT) applications such as traffic anomaly detection for smart transportation and medical diagnosis for smart healthcare. With the explosion of IoT data, anomaly detection on data streams raises higher requirements for real-time response and strong robustness on large-scale data arriving at the same time and various application fields. However, existing methods are either slow or application-specific. Inspired by the edge computing and generic anomaly detection technique, we propose an isolation forest based framework with dynamic Insertion and Deletion schemes (IDForest), which can incrementally update the forest to detect anomalies on data streams. Besides, IDForest is deployed on edge servers in parallel through packing each tree into a subtask, which facilitates the fast anomaly detection on data streams. Extensive experiments on both synthetic and real-life datasets demonstrate the efficiency and robustness of our framework for anomaly detection.

Soil Sampling Spacing Based on Precision Agriculture Variable Rate Fertilization of Pomegranate Orchard

ABSTRACT Variable rate fertilization is one of the core technologies of precision agriculture, and it is particularly important to develop a representative field sampling scheme with less workload. A grid sampling of 1 m × 1 m was conducted on a pomegranate garden of 414 m2 in Nanzhuang Town, Jianshui County, Honghe Hani and Yi Autonomous Prefecture, Yunnan Province, and set up 7 groups of sample point deletion schemes to study the reasonable sampling spacing of soil nutrients. Based on the grading standard of soil nutrients in plow layer, the distribution of soil nutrients was statistically analyzed by using classical statistical methods to reveal the spatial distribution variability of soil nutrients. We then utilized a geostatistical method to interpolate the soil nutrient to obtain the soil nutrient distribution map, and compared the soil nutrient distribution map of the deletion sampling scheme with the original sampling scheme A in shape and area. Results revealed that the deletion scheme E was the most consistent with the sampling results of the full coverage sampling scheme A, and the number of soil sampling could be reduced by half under the premise of meeting the requirements of precise fertilization. The field experiment showed that the variable rate fertilization could save 26%, 54% and 34% nitrogen, phosphorus and potassium fertilizer compared with the farmers’ conventional fertilization, the total yield of soft seed pomegranate increased by 41.52%, and the sugar content, sugar–acid ratio and vitamin C content of pomegranate grain increased by 11.79%, 51.26% and 31.59%. The results also provide support to determine sampling spacing, spatial variability of soil nutrients and variable rate fertilization in precision farming.

An Analysis of Rule Deletion Scheme in XCS on Reinforcement Learning Problem

The XCS classifier system is an evolutionary rule-based learning technique powered by a Q-learning like learning mechanism. It employs a global deletion scheme to delete rules from all rules covering all state-action pairs. However, the optimality of this scheme remains unclear owing to the lack of intensive analysis. We here introduce two deletion schemes: 1) local deletion, which can be applied to a subset of rules covering each state (a match set), and 2) stronger local deletion, which can be applied to a more specific subset covering each state-action pair (an action set). The aim of this paper is to reveal how the above three deletion schemes affect the performance of XCS. Our analysis shows that the local deletion schemes promote the elimination of inaccurate rules compared with the global deletion scheme. However, the stronger local deletion scheme occasionally deletes a good rule. We further show that the two local deletion schemes greatly improve the performance of XCS on a set of noisy maze problems. Although the localization strength of the proposed deletion schemes may require consideration, they can be adequate for XCS rather than the original global deletion scheme.

병렬 TCAM 기반의 IP 주소 검색에서 신속한 프리픽스 삭제

본 논문에서는 병렬 TCAM을 기반으로 한 IP 주소 검색 구조에서 프리픽스를 신속하게 삭제하는 기법을 제안한다. 기존의 삭제 기법들은, 프리픽스의 순서 유지와 가용 메모리 공간의 연속성 유지를 위해 한 차례 이상의 메모리 이동을 필요로 하고 있다. 본 기법에서는 삭제 시 TCAM에서 실제 메모리를 이동하지 않고, SRAM으로 구현된 스택을 사용하여 삭제된 프리픽스의 주소를 이 스택에 저장한다. SRAM은 TCAM에 비해 접근시간이 매우 짧기 때문에, 제안된 기법이 신속한 갱신을 수행할 수 있다. 그리고 실제 사용되는 포워딩 테이블과 갱신 내용을 적용한 실험을 통해, 프리픽스 삽입과 삭제에 따른 메모리 접근 시간의 관점에서 제안된 기법의 성능을 평가한다. 또한 상대적으로 매우 작은 크기의 스택을 사용해도 좋은 성능을 나타내고 있음을 실험 결과를 통해 제시한다. In this paper, we propose a technique which makes it faster to delete prefixes in an IP address lookup architecture based on parallel TCAMs. In previous deletion schemes, more than one memory movement is needed for the prefix ordering and keeping the available memory space consecutive. For deletion, our scheme stores the address of the deleted prefix in a stack implemented by SRAM instead of actual movement in TCAM. Since SRAM has very short latency compared to TCAM, the proposed scheme can accomplish fast updating. From the experiment with the real forwarding table and update trace, we evaluate the performance of our scheme in terms of the memory access time for the prefix insertion and deletion. The experiment result also shows good performance with considerably small size of stack.

Free-energy landscapes from adaptively biased methods: Application to quantum systems

Several parallel adaptive biasing methods are applied to the calculation of free-energy pathways along reaction coordinates, choosing as a difficult example the double-funnel landscape of the 38-atom Lennard-Jones cluster. In the case of classical statistics, the Wang-Landau and adaptively biased molecular-dynamics (ABMD) methods are both found efficient if multiple walkers and replication and deletion schemes are used. An extension of the ABMD technique to quantum systems, implemented through the path-integral MD framework, is presented and tested on Ne38 against the quantum superposition method.

How Many Accelerometer Days are Needed for Stable Population and Individual Weekly Activity Estimates?

PURPOSE: To determine number and distribution of monitoring days required to produce stable individual and population level estimates of a true 7 day mean for common accelerometer parameters. METHODS: Data for n=2532 adults (age≥20yrs) participating in the 2003-6 NHANES activity monitor protocol with ≥10 hrs wear for 7 days were analyzed. Accelerometer parameters included: 1) Moderate-to-vigorous physical activity (MVPA) minutes (≥2020 cnt/min); 2) bouted MVPA minutes; 3) sedentary minutes (<100 cnt/min); 4) total counts; and 5) average counts/min wear. Both random and systematic day deletion schemes were examined, including keeping 1 to 6 days at random, keeping Saturday + 1 to 5 random weekdays, keeping Sunday + 1 to 5 random weekdays, keeping 1 weekend day at random + 1 to 5 weekdays, and keeping both weekend days + 1 to 4 random weekdays. The above 25 deletion schemes were bootstrapped with 250 independent samples drawn from the master 7 day dataset for each deletion scheme. For each of n=250 bootstraps per deletion scheme, individual and group level means for the 5 outcomes were calculated and saved. To compare group level outcomes, percent difference was calculated between the true 7 day mean and the mean from each deletion scheme [((true mean - deletion mean)/ true mean)*100]. RESULTS: MVPA is used as an example for presentation. Similar trends were observed across variables except sedentary minutes, which remained relatively stable across all deletion schemes. Population MVPA for any 1 to 6 random days ranged from 20.0±0.3 to 20.0±0.0 with a mean bias ranging from -0.1±0.8% to 0.2±1.0%. Population MVPA for the 19 deletions with non random components ranged from 18.6±0.1 to 20.6±0.0 with a mean bias ranging from -3.0±0.0% to 7.2±0.5%. Similar to population results, individual bias is minimized for any 1 to 6 random days and observed to increase for deletions with non-random components. CONCLUSIONS: Simulation data suggest that stable estimates of population means can be obtained from a single randomly selected day of monitoring from a sampled week. However, bias is introduced in both population and individual estimates based on non-random selection of weekend days. Purposeful sampling of adults which forces inclusion of weekend data in analyses should be discouraged.

Stimulus size dependence of information transfer from retina to thalamus

Relay cells in the mammalian lateral geniculate nucleus (LGN) are driven primarily by single retinal ganglion cells (RGCs). However, an LGN cell responds typically to less than half of the spikes it receives from the RGC that drives it, and without retinal drive the LGN is silent (Kaplan and Shapley, 1984). Recent studies, which used stimuli restricted to the receptive field (RF) center, show that despite the great loss of spikes, more than half of the information carried by the RGC discharge is typically preserved in the LGN discharge (Sincich et al., 2009), suggesting that the retinal spikes that are deleted by the LGN carry less information than those that are transmitted to the cortex. To determine how LGN relay neurons decide which retinal spikes to respond to, we recorded extracellularly from the cat LGN relay cell spikes together with the slow synaptic (‘S’) potentials that signal the firing of retinal spikes. We investigated the influence of the inhibitory surround of the LGN RF by stimulating the eyes with spots of various sizes, the largest of which covered the center and surround of the LGN relay cell's RF. We found that for stimuli that activated mostly the RF center, each LGN spike delivered more information than the retinal spike, but this difference was reduced as stimulus size increased to cover the RF surround. To evaluate the optimality of the LGN editing of retinal spikes, we created artificial spike trains from the retinal ones by various deletion schemes. We found that single LGN cells transmitted less information than an optimal detector could.

A new networking model for biological applications of ad hoc sensor networks

In this paper, we introduce the Shared Wireless Infostation Model (SWIM), which extends the Infostation model by incorporating information replication, storage, and diffusion into a mobile ad hoc network architecture with intermittent connectivity. SWIM is able to reduce the delay of packet delivery at the expense of increased storage at the network nodes. Furthermore, SWIM improves the overall capacity-delay tradeoff by only moderately increasing the storage requirements. This tradeoff is examined here in the context of a practical application-acquisition of telemetry data from radio-tagged whales. To reduce the storage requirements, without affecting the network delay, we propose and study a number of schemes for deletion of obsolete information from the network nodes. In particular, through the use of Markov chains, we compare the performance of five such storage deletion schemes, which, by increasing the computational complexity of the routing algorithm, mitigate the storage requirements. The results of our study will allow a network designer to implement such a system and to tune its performance in a delay-tolerant environment with intermittent connectivity, as to ensure with some chosen level of confidence that the information is successfully carried through the mobile network and delivered within some time period.

Effects of Two Telegraphic Deletion Schemes On the Reading Behavior of Braille Readers 1

This study is part of a programmatic research effort which is attempting to develop a telegraphic writing style that will increase the ease of braille reading. Three types of literature were reduced by either 20% or 40% of their words using either a subjective or a computerized frequency method. The passages were read by 36 braille readers from grades 8 to 12. Reading performance was assessed by a multiple choice comprehension test, reading rate, and reading time. No significant decrements in comprehension were obtained either as a function of the type of deletion scheme or level of deletion. Reading rates tended to be slower for the 40% passages than for the 20% ones. Increased information density and unfamiliarity with telegraphic prose may have contributed to the slower reading rates.