Abstract:
A endpoint load rebalancing controller, method of controlling endpoint activity to suppress side channel variation and computer program product for controlling endpoint activity for suppressing side channel variation in information from utility company users, e.g., from power company endpoints. The load rebalancing controller monitors period to period endpoint service usage and predicts next period endpoint service usage. Whenever the controller maintains determines that the endpoint usage will exhibit a change that may be sufficient to convey activity information in side channel activity, the controller rebalances activity for the next period. Rebalancing may include shifting off-line execution from one period to another and capping or increasing on-line execution activity.

Description:
CROSS REFERENCE TO RELATED APPLICATION 
       [0001]    The present invention is a continuation of U.S. patent application Ser. No. 14/036,220 (Attorney docket No. YOR920130458US1), “ENDPOINT LOAD REBALANCING CONTROLLER” to John M Cohn et al.; and related to U.S. patent application Ser. No. 14/036,175 (Attorney docket No. YOR920130457US1), “SMART METER SECURITY SYSTEM AND METHOD” to John M Cohn et al., filed Sep. 25, 2013, assigned to the assignee of the present invention and incorporated herein by reference. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    1. Field of the Invention 
         [0003]    The present invention is related to information security and more particularly to differential power analysis and other side channel attacks (SCA). 
         [0004]    2. Background Description 
         [0005]    Increasingly, utility companies are deploying endpoint monitoring devices, known as smart meters, grid health sensors, and data concentrators, that monitor local endpoint power consumption and periodically report usage. As of 2010 there were eight (8) million smart meters deployed with as many as sixty (60) million expected to be deployed by 2020. Security and privacy is of great concern both personally and in the business-place. Consequently, smart endpoint devices have become security attack targets. Utility companies have employed encryption based design techniques to provide some security for smart meter communications. 
         [0006]    So for example, to prevent brute force security attacks on smart grid endpoints, some state of the art designs have incorporated encryption standardized in Advanced Encryption Standard (AES), e.g., AES-128,256. Some of these protection techniques are directed at preventing endpoint cryptographic key extraction. Others prevent reverse-engineering endpoint communication protocols. Since not all smart endpoint device communication is encrypted, providers have deployed meter reprogramming with embedded security technology, derived from financial transactions and government applications. Some embedded products have physical attack-detection mechanisms. Other embedded products rely on deployed logical techniques like lockable and encrypted, secure on-chip memories. Still other approaches rely on secure bootloaders that lock the endpoint device during manufacturing. Whenever financial or political incentives have aligned, however, someone has quickly developed some method, e.g., data mining technique, to exploit any available data. 
         [0007]    In spite of employing these security measures, using smart meters has added privacy and security vulnerabilities to what are commonly known as side channel attacks, which may reveal key information in spite of security efforts. For example, a smart meter may store or cache energy use information before reporting it to the service provider. State of the art smart meters monitor power consumption with a high resolution level, e.g., to the minute or even second. Stored information is an information-rich side channel, that characterizes customer habits and behaviors. 
         [0008]    Some activities have detectable power consumption signatures, e.g., watching television. Even detecting the presence or absence of activity can provide some information. Side channel attacks frequently use energy profiling to extract available consumption signatures, and exploit vulnerabilities that are beyond protection with encryption. Typical energy profiling includes, for example, Differential Power Analysis (DPA) and Differential Electromagnetic Analysis (DEMA), and also invasive attacks (e.g. laser attacks). Information embedded in power consumption data, increasingly, has made utility companies a potential source of privacy abuse by side channel attackers. Consequently, side channel attacks have raised privacy and security concerns both for home and business and concern for side channel attack vulnerability has been increasing, not only from the customer information privacy perspective but also for enterprise applications. 
         [0009]    Thus, there is a need for side channel attack security/prevention for protecting service facility infrastructure, and for focusing security on differential power and EM side channel attacks in smart meters and on preventing the attacks, and especially on smart meters metering and monitoring utility usage such as electricity, gas, water, fuel and other commodities. 
       SUMMARY OF THE INVENTION 
       [0010]    A feature of the invention is improved prevention of usage data based security breaches; 
         [0011]    Another feature of the invention is endpoint load rebalancing to protect from side channel attacks; 
         [0012]    Yet another feature of the invention is suppression of endpoint differential power and EM information conveyed in side channel activity; 
         [0013]    Yet another feature of the invention is endpoint monitoring and selective activity management to maintain endpoint load balance for preventing differential power and EM side channel attacks. 
         [0014]    The present invention relates to a endpoint load rebalancing controller, method of controlling endpoint activity to suppress side channel variation and computer program product for controlling endpoint activity for suppressing side channel variation in information from utility company users, e.g., from power company endpoints. The load rebalancing controller monitors period to period endpoint service usage and predicts next period endpoint service usage. Whenever the controller maintains determines that the endpoint usage will exhibit a change that may be sufficient to convey activity information in side channel activity, the controller rebalances activity for the next period. Rebalancing may include shifting off-line execution from one period to another and capping or increasing on-line execution activity. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0015]    The foregoing and other objects, aspects and advantages will be better understood from the following detailed description of a preferred embodiment of the invention with reference to the drawings, in which: 
           [0016]      FIG. 1  shows an example of a typical location with area supplier infrastructure, e.g., power company infrastructure, serving the location, according to a preferred embodiment of the present invention; 
           [0017]      FIG. 2  shows an example of a block diagram example of a preferred smart meter, which may be paired with or include a preferred on-board controller, e.g., as a system on a chip; 
           [0018]      FIGS. 3A-D  show an example of raw customer data and reported data; 
           [0019]      FIG. 4  show an example of a preferred on-board side channel controller controlling site operations according to a preferred embodiment of the present invention; 
           [0020]      FIGS. 5A-B  show an example of a global activity table and a task profile table used for estimating unit and overall side channel spike for individual tasks; 
           [0021]      FIG. 6  shows an example of a operation of a preferred on-board side channel controller controlling site operations; 
           [0022]      FIG. 7A  shows an example of projecting side channel activity; 
           [0023]      FIG. 7B  shows an example of pseudo-code for reallocating, capping and reordering on-line and off-line task execution; 
           [0024]      FIGS. 8A-B  show an example of projected side channel activity, and as reordered and capped. 
       
    
    
     DESCRIPTION OF PREFERRED EMBODIMENTS 
       [0025]    As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
         [0026]    Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. 
         [0027]    A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. 
         [0028]    Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. 
         [0029]    Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
         [0030]    Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
         [0031]    These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
         [0032]    The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
         [0033]    Turning now to the drawings and more particularly,  FIG. 1  shows an example of a typical location  100  with area supplier infrastructure, e.g., power company  102  infrastructure, serving the location  100 . A typical location  100  as in this example includes industrial zones  104 , commercial zones  106  and residential zones  108 . Supplier infrastructure includes one or more computer  110  receiving local usage information from local smart meters  112 . One or more units may include an on-board (and in some instances on-chip) rebalancing controller  114 , alone or in combination with a local smart meter  112 , monitoring and managing power grid  116  usage of supplier provided services, e.g., power used at individual residences  118 , commercial consumption at office buildings  120  and industrial consumption at local plants  122 . 
         [0034]    Previously, side channel attackers created detailed profiling capabilities to exploit hidden information embedded in available high resolution usage data. Burglars could use energy profiling, for example, to extract information to determine a homeowners comings and goings, e.g., vacancies both daily (e.g., work schedules) and extended (e.g., vacations). Computer activity, for example, can vary depending on what tasks the computer is performing, e.g., whether the computer is number crunching or idle. A typical state of the art processor (and computer) uses much more power when it is active than when it is idle. Determining that difference can reveal activity that is not otherwise intended to be public. Accordingly, differential power and electromagnetic (EM) attacks collecting side-channel (power usage) data over long periods of time and have been successful in extracting signatures that reveal key information on both processing activity and data. 
         [0035]    An industrial spy could have used energy profiling to extract activity signatures for more serious implications for an enterprise customer. The spy could use the business&#39;s power dissipation profile(s) to reveal critical information on enterprise activity, even minute to minute activity. For example, using the proper analysis tools, one can extract critical information buried in a bank&#39;s power usage, information such as trading scheme timing, trading duration, trading activity start and end, and trading patterns. Power dissipation and EM patterns may hold key manufacturing process information, trading algorithms and/or security vulnerability. If, a side-channel attacker identifies daily/weekly activity patterns an attacker can, for example, customize attacks to the activity patterns. 
         [0036]    Thus, a preferred an on-chip, or on-board rebalancing controller  114 , with or without a cooperating the smart meter  112 , rebalances activity and controls local activity to cap minimum and maximum detectable activity levels according to a preferred embodiment of the present invention. In particular, a preferred rebalancing controller  114  reads hardware activity counters regularly, rebalancing and capping activity levels for communications patterns, power usage, processing activity and anything else a particular user may specify, thereby preventing energy profiling and side-channel attacks. 
         [0037]    It is understood that although described for smart meters monitoring power usage, the present invention has application to data concentrators and other units for collecting metered information; and, anywhere that side channel attack vulnerabilities pose a threat to information security, personal, private and/or public. Moreover, the present invention has application beyond electric (smart) grids and related components, such as for metering and monitoring gas, water, fuel or other commodities. 
         [0038]      FIG. 2  shows a block diagram example of a preferred smart meter  112 , which may be paired with or include a preferred rebalancing controller  114 , e.g., as a system on a chip  1120 . Preferably, the core chip  1120  is based on an Advanced Reduced Instruction Set Computer (RISC) Machines (ARM) processor  1122  using Advanced Microcontroller Bus Architecture (AMBA)  1124  for on-chip functions communications. In addition a preferred chip  1120  may include, for example, storage  1126 ,  1128 ,  1130 , analog to digital converter (ADCs)  1132 , a micro direct memory access (μDMA) controller  1134 , an interrupt controller  1136  and timing  1138 ,  1140  and various input/output (I/O) controllers/ports  1142 ,  1144 ,  1146 ,  1148 . 
         [0039]    In this example, the storage includes random access memory (RAM)  1126 , read only memory (ROM)  1128  and flash memory  1130 , storing instructions, data and generic power usage patterns as appropriate. The RAM  1126 , preferably, is static RAM (SRAM). Timing includes a real time clock (RTC)  1138  and general-purpose timers  1140 . The I/O ports in this example include a universal serial bus (USB) port  1142 , two (2) general-purpose I/O (GPIO) ports  1144 , a universal asynchronous receiver/transmitter (UART)  1146  and a system packet interface (SPI)  1148 . 
         [0040]    A current sensor  1150  senses local current use and a voltage sensor  1152  senses local voltage fluctuations. Each of the sensors  1152  is connected to an ADC, with data from both used for determining power local power use. A local display  1154 , e.g., a seven (7) digit liquid crystal diode (LCD) display, indicates instantaneous power consumption. Communications processors, e.g., suitably enabled ARM processors, provide local and external communications capabilities and may be on the same chip  1120  or, as in this example, capabilities separate from the system chip  1120 . Thus, in this example, communications include a wireless local area network (WLAN or WiFi) capability  1156 , a Zigbee data communications capability  1158 , a cellular or wired modem capability  1160  and/or a power line network capability  1162 . 
         [0041]      FIGS. 3A-D  show an example of raw customer data and reported data. Thus,  FIG. 3A  shows an example of a customer consumption report  130  provided from a power company indicating cumulative monthly power consumption in kilowatt hours (kWh). As shown in  FIG. 3B , however, a preferred smart meter, e.g., 112, may measure 132 instantaneous power use, typically sampling power (kW) minute by minute. So as shown in  FIG. 3C , end node consumption data  134  collected, e.g., from a bank, by a smart meter may have some ambient level, with server power being observable during peak trading periods  136 . Moreover, that server power may be extracted  138  from the raw data as shown in  FIG. 3D . 
         [0042]    Thus, a side channel attacker can determine server activity from the raw data. By observing the beginning of the critical activity in smart metered power patterns or by observing equipment close to the end-node, activity patterns may indicate, for example, a trading activity period in the bank. An attacker can determine, for example, the bank&#39;s schedule and trading patterns, e.g., trading between 9:15-10:00 am and 2-3 pm. Encryption, as well as other standard protection techniques, have provided inadequate protection for shielding against this kind of attack, but are not suitable for protecting against differential power and EM attacks. 
         [0043]    However,  FIG. 4  shows an example of a preferred rebalancing controller  114  controlling site operations  140  to attenuate side channel variation for protecting against differential power and EM attacks according to a preferred embodiment of the present invention. In a typical endpoint some operations  142  occur in real time or on-line  142 - 1 ,  142 - 2 , . . . ,  142 -M, and other operations  144  occur in the background or off-line  144 - 1 ,  144 - 2 , . . . ,  144 -N. Each task and local operating unit provides, or is associated with, a side channel activity estimate  146 ,  148 , e.g., in a unit-level hardware profile table and a task profile table. The side channel activity estimates  146 ,  148  may include, for example, power, activity, EM and temperature for each task and unit. The preferred rebalancing controller  114  checks ongoing and projected location activity  146 ,  148  to identify when activity exceeds selected thresholds and/or when side channel activity exceeds selected limits. Based on periodic activity results, the preferred rebalancing controller  114  performs local load balancing and caps task decisions to selectively shift task activity  150 ,  152  between monitoring periods to obfuscate activity from side channel attacks. 
         [0044]    Activity balancing and capping  150 ,  152  may start when any activity, unit level activity or overall activity, causes usage to rise above, or fall below, specified usage thresholds, e.g., to a level sufficient that a side channel attacker could extract information on the activity by monitoring usage information. During balancing and capping  150 ,  152 , the rebalancing controller  114  uses power feedback information from location activity  146 ,  148  to reassign and/or reorder activity, both off-line  142  and on-line  144  activity. The rebalancing controller  114  reassigns and adjusts activity  142 ,  144  to minimize period to period power fluctuations and avoid detectable behavior within operation thresholds. Preferably, the rebalancing controller  114  limits period to period power fluctuations in unit and total power to normalize activity reflected in EM and thermal profiles. If the preferred rebalancing controller  114  is deployed with smart meter  112 , the rebalancing controller  114  may communicate critical activity to the smart meter  112 , further shielding endpoint communications and smart meter inputs/outputs (IOs). 
         [0045]      FIGS. 5A-B  show an example of a global activity table  160  and a task profile table  170  used for estimating unit and overall side channel spikes for individual tasks and overall. The global activity table  160  includes unit-level hardware profiles with an entry for each local unit  162 - 1 ,  162 - 2 , . . . ,  162 - n . Each unit entry  162 - 1 ,  162 - 2 , . . . ,  162 - n  includes a length for each of J on-line and off-line tasks  164 - 1 - 164 -J and an estimated side channel spike  166 - 1 - 166 -J for each respective task. Similarly, the task profile table  170  includes an entry for every task  172 - 1 ,  172 - 2 , . . . ,  172 -M/N (for M off-line tasks and N on-line tasks). Each task entry includes K operating conditions  174 - 1 - 174 -K and an estimated side channel spike for each respective operating condition  176 - 1 - 176 -K. If the configuration in the global activity table  160  indicates that activity or inactivity causes usage to fall out of preselected limits, e.g., spike above a maximum activity threshold, or below a minimum activity threshold, the rebalancing controller  114  adjusts, and scales, unit and/or local activity to even out spikes, thereby minimizing any information that might otherwise be revealed. 
         [0046]      FIG. 6  shows an example of a operation  180  of a preferred rebalancing controller  114  controlling endpoint operations ( 140  in  FIG. 4 ) according to a preferred embodiment of the present invention. The preferred rebalancing controller  114  monitors usage  182  for ongoing and projected location activity  146 ,  148  until usage for individual unit activity rises above, or falls below, preselected minimum and maximum (Min-Max) limits; or, usage for collective activity rises above, or falls below, preselected min-max limits. Whenever endpoint usage causes side channel activity to vary from the preselected min-max limits, the preferred rebalancing controller  114  controls hardware and software collaboratively, dynamically leveraging task characteristic information and detailed hardware characteristics to rebalance endpoint activity, adjusting usage to suppress discernable/discoverable side channel activity. 
         [0047]    So when usage for individual unit activity is not between the preselected limits, the rebalancing controller  114  marks  184  unit activity for side channel shielding; and, when usage for collective activity is not within the limits, the rebalancing controller  114  marks  186  overall activity for side channel shielding. Next the rebalancing controller  114  reallocates resource configurations  188  to rebalance projected load and updates the location activity table with the results. Then, the rebalancing controller  114  reorders activity  190  based on that reallocation, both on-line task  142 - 1 ,  142 - 2 , . . . ,  142 -M and off-line task  144 - 1 ,  144 - 2 , . . . ,  144 -N execution, and caps resulting activity as necessary to reduce usage variation between monitoring periods. 
         [0048]    Monitoring usage  182 , the rebalancing controller  114  iteratively selects  1820  each unit (i) from the n units, and reads the activity level  1822  for the selected unit. The rebalancing controller  114  estimates  1824  side channel activity (e.g., power usage levels) for the selected unit. Then, the rebalancing controller  114  checks  1826  whether the estimated side channel activity is within the preselected min-max limits (e.g., Max Activity Threshold ATU_mx, Min Activity Threshold ATU_mn). If not, the rebalancing controller  114  marks unit activity  184  for side channel shielding, rebalances  188  and reconfigures  190 . Otherwise, the rebalancing controller  114  updates  1828  an accumulated side channel activity usage total for all tasks in all units. The rebalancing controller  114  checks  1830  whether the accumulated estimated usage is within the preselected cumulative min-max limits (e.g., Activity Threshold ATU_Cmx, Activity Threshold ATU_Cmn). If not, the rebalancing controller  114  marks cumulative activity  186  for side channel shielding, rebalances  188  and reconfigures  190 . Otherwise, the rebalancing controller  114  begins the next iteration selecting  1820  another unit and reading activity usage level  1822 . 
         [0049]      FIG. 7A  shows an example of predicting side channel activity from endpoint usage ( 1824  and  1828  of  FIG. 6 ); and,  FIG. 7B  shows an example of pseudo-code for reallocating  188 , reordering and capping  190  on-line and off-line task execution with reference to controlling endpoint operations  140  in  FIG. 4 , and with like features labeled identically. As noted hereinabove, the rebalancing controller  114  estimates  1824  side channel activity (A) for every task (T) for each unit of the n units (U1, U2, . . . , Un), to estimate  146 ,  148  activity level vectors for both on-line and off-line tasks (e.g., &lt;T1A1, T1A2, . . . , T1An&gt;, &lt;T2A1, T2A2, . . . , T2An&gt;, . . . , &lt;TNA1, TNA2, . . . , TNAn&gt; or &lt;TMA1, TMA2, . . . , TMAn&gt;) for reallocating resources  188 . Reallocating resources  188  may include, for example, allocating voltage and frequency resources to minimize estimated side channel risks, for example, from spikes in side channel activity level. From these estimates the rebalancing controller  114  updates the global activity table  160  and determines  1826  from the updated table  160  whether the estimated side channel activity for the unit is within the preselected Min-Max limits. The rebalancing controller  114  also determines the accumulated side channel activity  1828  for all tasks in all units from the updates. Further, the rebalancing controller  114  rebalances endpoint/unit activity, e.g., based on predicted side channel voltage and frequency, and caps and reorders execution  190  based on the updated table  160 . 
         [0050]    Preferably, as shown in  FIG. 7B , the rebalancing controller  114  begins reordering and capping  190  by selecting a task mix to minimize the changes in side channel activity in sequential monitoring periods, where time (t) is measured in discrete periodic units, e.g., whole seconds, minutes, tens of minutes, hours or days. After selecting a task list ( 1820  in  FIG. 6 ) with on-line and off-line tasks, the rebalancing controller  114  estimates side channel activity (SC)  1822  to determine expected/projected side channel activity  1824  for the next period (SC t+1 ). 
         [0051]    The rebalancing controller  114  checks  1900  whether the projected activity in next period indicates an increase over current activity by some threshold (Δ). If the estimated change indicates an activity increase that is too large (i.e., SC t+1 &gt;SC t +Δ), then the rebalancing controller  114  delays  1902  one or more off-line tasks to the following period (t+2). After delaying off-line tasks  1902 , if necessary, the rebalancing controller  114  reduces or scales down  1904  rebalanced activity to reduce the period to period change below the threshold. 
         [0052]    Similarly, the rebalancing controller  114  checks  1906  whether the projected activity in next period indicates a decrease from current activity beyond the threshold. If the estimated change indicates too large of a decrease in activity (i.e., SC t+1 &gt;SC t −Δ), then the rebalancing controller  114  advances  1908  one or more off-line tasks to the next period from later following period(s), i.e., from (&gt;t+2) to (t+1). After advances delaying on-line tasks  1908 , the rebalancing controller  114  increases or scales up  1910  remaining activity to reduce the period to period change below the threshold. 
         [0053]      FIGS. 8A-B  show an example of projected side channel activity  200 , and as reordered and capped  202  by a preferred rebalancing controller. At interval  204  projected activity is below the minimum side channel threshold. The rebalancing controller  114  reorders execution to start off-line tasks and scale up activity for on-line tasks. Later, at interval  206  projected activity is projected to rise above the maximum side channel threshold. The rebalancing controller  114  reorders execution to suspend off-line tasks and scale down activity for on-line tasks. Thus, the side channel activity  202  from reordering and capping exhibits no discernable activity footprint. 
         [0054]    Advantageously, the present invention focuses on managing local resource usage to frustrate differential power and electromagnetic (EM) attacks, securing from side channel attacks both to, and independent of, smart meters. The present invention is compatible with existing data encryption services and devices to add protection from side channel attacks. Because a preferred rebalancing controller performs selective localized load balancing, even breaking an encryption key does not provide access to power information and patterns, reducing service provider customers&#39; vulnerability to a nefarious side channel information tapping. Because endpoint power is free from wide usage swings and spike, there are no discernable usage pattern changes and side channel attackers cannot detect periods of high endpoint activity or inactivity. Since side channel attackers continually observe a normalized usage pattern, even during periods of higher on-line activity, the attackers have little motive for expending efforts for more in-depth side channel observations. Even so, the provider receives complete, albeit normalized usage data, securely transmitted for better managing and supplying provider capabilities and services, e.g., over a smart grid. 
         [0055]    While the invention has been described in terms of preferred embodiments, those skilled in the art will recognize that the invention can be practiced with modification within the spirit and scope of the appended claims. It is intended that all such variations and modifications fall within the scope of the appended claims. Examples and drawings are, accordingly, to be regarded as illustrative rather than restrictive.